Summary generation apparatus, summary generation method, and program

Abstract

To properly generate a summary of a resume.SOLUTION: A summary generation apparatus includes: a summary operation receiving unit which receives, from a user, operation for generating a summary of information used for recruitment; an input information acquisition unit which acquires primary information which is used for generating a summary and has been already input by the user; a first summary generation unit which generates a summary through inference using a pre-trained machine learning model which uses the primary information acquired by the input information acquisition unit as input; a presentation unit which presents the summary generated by the first summary generation unit; a feedback information acquisition unit which acquires, from the user, feedback information on the summary presented by the presentation unit; and a second summary generation unit which generates a summary through inference using the pre-trained machine learning model which uses the primary information acquired by the input information acquisition unit and the feedback information acquired by the feedback information acquisition unit as input.SELECTED DRAWING: Figure 17

Description

Technical Field

[0001] The present invention relates to a summary generation device, a summary generation method, and a program.

Background Art

[0002] Conventionally, when an employer or the like newly hires an employee or the like, they may first make a preliminary judgment on whether to hire by looking at documents such as the resume and work history of the applicant. Therefore, the resume is useful for the employer or the like to judge whether to hire. On the other hand, for applicants and the like, creating a resume takes time and effort. Therefore, as a technology for assisting in creating a resume, a technology for creating an electronic resume with a photo on a network using a multimedia kiosk (MMK) or the like provided in a convenience store or the like is known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] According to the technology as described above, the creation of a resume is assisted by an applicant or the like (applicant) inputting information into a pre-prepared resume format. Here, for an employer who conducts the hiring business, it is difficult to check every corner of the resumes sent in large quantities. Therefore, in many cases, a "summary" column is provided in the resume format. However, it is difficult to accurately summarize the appealing points in the limited summary column. Therefore, there has been a demand for applicants and the like to automatically create a summary in which the appealing points are appropriately reflected in the resume, work history, and the like.

[0005] The present invention has been made in view of such circumstances, and an object thereof is to provide a summary generation device, a summary generation method, and a program capable of suitably generating a summary of a resume or a work history summary.

Means for Solving the Problems

[0006] (1) One aspect of the present invention includes a summary operation reception unit that receives an operation from a user for generating a summary of information used for personnel recruitment, an input information acquisition unit that acquires primary information, which is information used for generating the summary and has already been input by the user, a first summary generation unit that generates a summary by performing inference using a machine learning model that has been pre-trained with the primary information acquired by the input information acquisition unit as input, a presentation unit that presents the summary generated by the first summary generation unit, a feedback information acquisition unit that acquires feedback information about the summary presented by the presentation unit from the user, and a second summary generation unit that generates a summary by performing inference using a machine learning model that has been pre-trained with the primary information acquired by the input information acquisition unit and the feedback information acquired by the feedback information acquisition unit as input.

[0007] (2) Further, in one aspect of the present invention, in the summary generation device described in (1) above, the feedback information at least includes information on whether the user is satisfied with the summary presented by the presentation unit, and when the user is not satisfied, it further includes information on the reason for dissatisfaction.

[0008] (3) Further, in one aspect of the present invention, in the summary generation device described in (2) above, the second summary generation unit takes as input the primary information acquired by the input information acquisition unit and the reason for dissatisfaction of the user included in the feedback information acquired by the feedback information acquisition unit, and generates a summary by performing inference using a machine learning model that has been pre-trained.

[0009] (4) Further, in one aspect of the present invention, in the summary generation device described in (3) above, the second summary generation unit selects machine learning models that have been pre-learned based on different teacher data respectively, based on the reasons for dissatisfaction of the user included in the feedback information acquired by the feedback information acquisition unit, and generates a summary by performing inference using the selected machine learning model.

[0010] (5) Further, one aspect of the present invention includes a summary operation reception step of receiving an operation from a user regarding generating a summary of information used for human resource recruitment, an input information acquisition step of acquiring primary information which is information used for generating the summary and has already been input by the user, a first summary generation step of generating a summary by performing inference using a machine learning model that has been pre-learned with the primary information acquired in the input information acquisition step as an input, a presentation step of presenting the summary generated in the first summary generation step, a feedback information acquisition step of acquiring feedback information about the summary presented in the presentation step from the user, and a second summary generation step of generating a summary by performing inference using a machine learning model that has been pre-learned with the primary information acquired in the input information acquisition step and the feedback information acquired in the feedback information acquisition step as inputs.

[0011] (6) Also, one aspect of the present invention is a computer, which includes a summary operation reception step of receiving from a user an operation for generating a summary of information used in personnel recruitment, an input information acquisition step of acquiring primary information, which is information used for generating the summary and has already been input by the user, a first summary generation step of generating a summary by performing inference using a machine learning model that has been pre-trained with the primary information acquired in the input information acquisition step as input, a presentation step of presenting the summary generated in the first summary generation step, a feedback information acquisition step of acquiring from the user feedback information about the summary presented in the presentation step, and a second summary generation step of generating a summary by performing inference using a machine learning model that has been pre-trained with the primary information acquired in the input information acquisition step and the feedback information acquired in the feedback information acquisition step as input. It is a program for causing the above to be executed.

Effect of the Invention

[0012] According to the present invention, it is possible to provide a summary generation device, a summary generation method, and a program that can suitably generate a summary of a resume.

Brief Description of the Drawings

[0013]

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Mode for Carrying Out the Invention

[0014] [Embodiment] Hereinafter, a preferred embodiment of a summary generation device, a summary generation method, and a program according to an aspect of the present invention will be described in detail with reference to the accompanying drawings. Note that the aspects of the present invention are not limited to these embodiments, and also include those with various modifications or improvements. That is, the components described below include those that can be easily assumed by those skilled in the art and substantially identical ones, and the components described below can be combined as appropriate. Also, various omissions, substitutions, or changes of the components can be made without departing from the gist of the present invention. In the following drawings, in order to make each configuration easy to understand, the scale, number, etc. in each structure may be different from those in the actual structure. In the embodiment described below, the invention will be described by taking the summary of a resume as an example. However, this embodiment is not limited to this example and can be replaced with other application documents such as a resume.

[0015] [First Embodiment] First, a first embodiment will be described with reference to FIGS. 1 to 14.

[0016] FIG. 1 is a diagram showing an example of the configuration of a system according to the first embodiment. With reference to this figure, an example of the configuration of system 1 according to this embodiment will be described. System 1 provides a job seeking support service to job seekers who are considering employment (including re-employment and job transfer, etc.). The job seeking support service includes a job transfer support service. An employer of a company considering the employment of employees may request a job seeker who is considering a job transfer to the company to submit a resume (also referred to as a resume. The same applies in the following description). System 1 according to this embodiment supports the job transfer of job seekers by automatically generating a resume through a relatively simple operation by the job seeker.

[0017] System 1 is configured to include a summary generation device 10 and a plurality of user terminal devices 30. In an example shown in the figure, as an example of the plurality of user terminal devices 30, user terminal devices 30-1, 30-2, …, 30-n (n is a natural number of 1 or more) are shown. The plurality of user terminal devices 30 are each connected to the summary generation device 10 via a predetermined communication network NW. The communication network NW includes the Internet, WAN (Wide Area Network), LAN (Local Area Network), public line, provider device, dedicated line, wireless base station, and the like.

[0018] The user terminal device 30 is operated by a user. The user terminal device 30 may be, for example, a smartphone, a tablet terminal, or a notebook computer. The user is a job seeker who is looking for an employment destination (including re-employment destination, job transfer destination, etc.) using System 1. The user performs a search for an employment destination by operating the user terminal device 30. Note that the search for an employment destination performed by the user includes inputting the user's resume and having the employer search for the user.

[0019] The summary generation device 10 operates by executing a summary generation program. The summary generation device 10 generates a summary of the resume based on the information input by the user terminal device 30. Note that the summary generation program may be implemented as a Web application (Web app), or may be distributed as an application to the user terminal device 30, installed in each user terminal device 30, and operate on each user terminal device 30.

[0020] FIG. 2 is a diagram showing an example of the screen configuration of the primary information input screen according to the first embodiment. The summary generation program causes, for example, various display screens to be displayed on a display device unit (not shown) provided in the user terminal device 30, and acquires the data selected or input by the job seeker on the screen. Examples of the various display screens include, for example, a work history input screen, a skill selection screen, and an appeal point selection screen. Each of the various screens will be described in order below.

[0021] Figure 2(A) is an example of an employment history input screen and is an example of a display screen for inputting the most recent employment history among the job seeker's work history. The employment history input screen shown in this figure includes, as input fields for selecting or inputting data, illustratively, an occupation input field 4a, an affiliated company input field 4b, an employment type input field 4c, and an employment period input field 4d.

[0022] The most recent occupation of the job seeker is input into the occupation input field 4a. In the example shown in the figure, it is configured to be possible to select the most recent occupation of the job seeker from among predetermined occupations. As options for occupations, for example, sales, general affairs, planning, clerical work, accounting, human resources, sales, customer service, development, and technology, etc. can be provided. However, this embodiment is not limited to this example, and necessary occupations can be appropriately provided according to the actual operation.

[0023] The name of the company that the job seeker most recently belonged to or belongs to is input into the affiliated company input field 4b. Since the number of company names is huge and it is not easy to make it a selection input like the occupation input field 4a, it is preferable for the job seeker to input the company name as natural language. However, this embodiment is not limited to this example, and it may be a selection input according to the actual operation, or a database for managing company information may be provided within the system 1, or by connecting to a similar database existing outside the system 1 via the network NW, it may be possible to search for and determine the official company name by only inputting a part of the company name into the affiliated company input field 4b.

[0024] The employment type in the company entered in the affiliated company input field 4b is input into the employment type input field 4c. In the example shown in the figure, it is configured to be possible to select an arbitrary employment type from among predetermined employment types. As options for employment types, for example, full-time employee, contract employee, dispatched employee, part-time job, and part-time work, etc. can be provided. However, this embodiment is not limited to this example, and necessary employment types can be appropriately provided according to the actual operation.

[0025] During the employment period, in the input field 4d, a check mark indicating whether the applicant is currently employed by the company entered in the affiliated company input field 4b, and the start and end months of the employment period are entered. Note that the end month can only be entered when not currently employed, and may not be entered when currently employed.

[0026] The input items provided on the work history input screen are not limited to the illustrated example. For example, items related to work history such as annual income at the affiliated company may be appropriately entered. In addition to the work history input screen, for example, a screen for entering the job seeker's resume, including an academic history input screen for entering the job seeker's academic background, may be appropriately displayed. Further, when there is no work experience, a screen for entering information related to the job the job seeker desires may be displayed.

[0027] Figure 2(B) is an example of a skill selection screen and an example of a screen for entering a job seeker's skills. On the skill selection screen, questions and options related to skills are displayed. On the skill selection screen shown in the figure, illustratively, as questions related to skills, Question 5a, Question 5c, Question 5e, and Question 5g are displayed. Also, as options for each question, Option 5b, Option 5d, and Option 5f are displayed.

[0028] The questions and options related to skills are created in advance for each job type. Therefore, for example, based on the job type entered in the job type input field 4a of the work history input screen shown in Figure 2(A), it is preferable to display on the skill selection screen the questions and options related to skills stored in association with that job type.

[0029] The skill selection screen allows job seekers to input skills by having them select one or more applicable options from the options displayed on the screen. At this time, the selected options should be displayed in a form that is easy to visually recognize as being selected, such as by making the background color different from the unselected options (it is desirable to make it stand out). By adopting such a design, not only can more options be listed in a limited space compared to the case of displaying options using a pull-down menu or checkboxes, but also the selection status (whether it is being selected or not) can be displayed in a more visually recognizable state. Note that the skills that the skill selection screen allows job seekers to select can include various experiences such as those related to industry, business, or department in addition to technical capabilities, as well as those related to knowledge and education such as qualifications or degrees held. Job seekers can easily clarify their skills by selecting one or more applicable options from the options according to the questions displayed on the skill selection screen.

[0030] Specifically, Question 5a states, "What tools do you have development / operation experience with in the security system?" The corresponding options 5b are "AWVS", "Nessus", "SqlMap", "Nmap", "Appscan", "Burp Suite", "X-Way", "AXIOM", and "Others".

[0031] Specifically, Question 5c states, "What regulations / frameworks do you have development / operation experience with in security system development?" The corresponding options 5d are "ISO27001", "PCI-DSS", "CCPA", "Revised Personal Information Protection Law", and "Others".

[0032] Specifically, Question 5e states, "What target operating systems do you have development / operation experience with?" The corresponding options 5f are "Windows", "MacOS", "Linux (registered trademark)", and "Other Unix".

[0033] Specifically, for Question 5g, it is stated as "What languages do you have development experience in?" The corresponding options may be "HTML·CSS", "PHP", "Ruby", "Visual Basic", "Java Script", "Python", and "C#", etc. However, due to the limited display area of the screen, they are not shown in the figure. The job seeker can view the presented options by scrolling the screen.

[0034] In addition, for the options displayed on the skill selection screen, further subordinate concept options may be provided so that the answer to the question can be selected from among the subordinate concept options. In this case, by operating the upper-level option, the subordinate options corresponding to the upper-level option may be configured to be viewable.

[0035] Figure 2(C) is an example of an appeal point selection screen and an example of a screen for job seekers to enter their appeal points. On the appeal point selection screen, a plurality of selectable candidates for appeal points are displayed. The job seeker enters the appeal points by selecting one or more corresponding ones from among the plurality of candidates displayed on the screen.

[0036] As candidates for the appeal points displayed on the appeal point selection screen, for example, the skills selected on the skill selection screen described with reference to Figure 2(B) can be used. In this way, the job seeker can appropriately convey to the employer the skills that the job seeker particularly wants to appeal or is confident in among the skills the job seeker has selected by selecting one or more skills to appeal from among the skills the job seeker has selected.

[0037] On the appeal point selection screen shown in the figure, as candidates for the selectable appeal points, illustratively, candidate 6a regarding the job seeker's specialized skills, candidate 6b regarding the qualifications and degrees the job seeker holds, candidate 6c regarding the industries and departments the job seeker has experience in, and candidate 6d regarding the job seeker's specific work experience are displayed.

[0038] Note that the candidates for the appeal points are not limited to the skills selected on the skill selection screen. For example, information that the job seeker has input in the past, such as information regarding the job seeker's work history, may be included as candidates for the appeal points. Also, skills that are subordinate concepts belonging to the skills selected on the skill selection screen may be added as candidates for the appeal points.

[0039] Specifically, as candidates 6a regarding the job seeker's specialized skills, "Docker", "Docker", "Go", "GCS", "Agile / Scrum", "Git", "GCE", and "GKE" are listed as candidates.

[0040] Also, as candidate 6b regarding the qualifications and degrees that the job seeker holds, "AWS Certified Security" is listed as a candidate.

[0041] Also, as candidate 6c regarding the industries and departments that the job seeker has experience in, "Service" is listed as a candidate.

[0042] Also, as candidate 6d regarding the job seeker's specific work experience, "IAM", "Requirement Definition", and "Design" are listed as candidates.

[0043] The job seeker can select 5 appeal points from among these candidates. Note that the number of selectable appeal points is not limited to 5 as an example, but it is preferably finite. If the number of appeal points is too large, the points to be appealed will become blurred, and it will not be easy to create a summary of the resume. In particular, when there is a character limit (including the case of a guideline) for the summary of the resume, it is particularly preferable to limit the number of appeal points.

[0044] FIG. 3 is a flowchart showing an example of the operation flow of the summary generation device according to the first embodiment. While referring to this figure, a series of processes performed by the summary generation device 10 will be described. Also, the configuration of the display screen displayed on the user terminal device 30 in each step will be described as appropriate with reference to FIGS. 4 to 10. Note that the display screen is an example when the user terminal device 30 is a smartphone. The display screen may perform a display suitable for the screen size when the user terminal device 30 is a tablet terminal or a notebook personal computer.

[0045] (Step S110) First, the summary generation device 10 displays a job summary input screen on the user terminal device 30. The user can directly input a summary of the work history document (hereinafter, may be simply referred to as a job summary) on the screen using the touch panel, voice input device, etc. of the summary generation device 10.

[0046] FIG. 4 is a diagram showing an example of the screen configuration of the job summary input screen according to the first embodiment. In step S110, a display screen D1 as shown in this figure is displayed. On the display screen D1, the user edits the job summary. The display screen D1 has reference signs D101, D102, and D103 as the screen configuration. Reference sign D101 is a text box into which the user can directly input a job summary. The user enters his / her past experience, things to appeal, etc. in the text box indicated as reference sign D101. Input of 400 characters is recommended as a target for the text box.

[0047] Reference sign D102 is an AI summary generation execution button. The user starts the AI summary by operating this button. The outline of the AI summary process will be described later.

[0048] Reference sign D103 is a temporary save button. The user temporarily saves the job summary directly input by the user, the job summary generated by the AI summary, or the job summary in which the job summary generated by the AI summary is corrected by the user by operating this button.

[0049] (Step S120) Return to FIG. 3, and the summary generation device 10 receives an AI summary generation execution operation from the user. The AI summary generation execution operation is received, for example, when the user presses the AI summary generation execution button of the above-described reference sign D102. Here, when the summary generation device 10 receives an AI summary generation execution operation from the user, it may present precautions for use and seek consent from the user.

[0050] FIG. 5 is a diagram showing an example of the screen configuration of the AI summary generation execution screen according to the first embodiment. As shown in the drawing, when the summary generation device 10 receives an AI summary generation execution operation from the user, it displays a display screen D2 presenting precautions for use and seeks consent from the user.

[0051] In reference sign D201, "Precautions for Use" is described. Specifically, · Please note in advance that the AI summary function may be subject to specification changes, discontinuation, etc. without prior notice to customers. · This function may display inappropriate expressions or information not related to job experience, and may not be able to create a job summary that meets your expectations. · This function can be used up to 10 times a day (reset at 24:00). The above precautions are described.

[0052] If the user agrees to the precautions described in reference sign D201, the user presses the start use button of reference sign D202 to proceed with the process.

[0053] (Step S130) Return to FIG. 3, and when an AI summary generation execution operation is performed, the summary generation device 10 determines whether the AI summary generation can be executed. The AI summary generation is performed only when the matters described with reference to FIG. 2 are sufficiently input in advance. In other words, the summary generation device 10 refers to the input status of the matters described with reference to FIG. 2 and determines whether sufficient information for generating a summary of the work history has been input.

[0054] (Step S140) Specifically, when the summary generation device 10 refers to the input status of the matters described with reference to FIG. 2 and determines that sufficient information for generating the summary of the work history has been input (that is, Step S140; no shortage), it determines that it is executable and advances the process to Step S190. Further, when the summary generation device 10 determines that sufficient information for generating the summary of the work history has not been input (that is, Step S140; shortage), it determines that it is not executable and advances the process to Step S150.

[0055] (Step S150) When sufficient information for generating the summary of the work history has not been input, the summary generation device 10 displays a "not generable screen" for the user. Buttons for enriching the resume are also displayed on the display screen, and the user can proceed to the resume input screen by operating the buttons.

[0056] FIG. 6 is a diagram showing an example of the screen configuration of the not generable screen according to the first embodiment. As shown in the figure, when sufficient information for generating the summary of the work history has not been input, the summary generation device 10 displays a display screen D3, which is a not generable screen, for the user. The display screen D3 includes, as components of the screen, a symbol D301 as a presentation unit for informing the user that summary generation is not possible due to insufficient information, and a symbol D302, which is a button for enriching the resume. When the summary generation device 10 detects that the symbol D302 has been operated, it proceeds to the next process.

[0057] (Step S160) Return to FIG. 3, and the summary generation device 10 displays a "resume input promotion screen" for the user. Specifically, when generating a summary, in order to prompt input for items that are highly important and preferably already input when generating the summary but for which the user has not yet input, a question screen is displayed for the user regarding the items for which the user has not input. The user expands the resume by inputting information according to the instructions on the displayed screen. By adopting such a form, the user does not need to wonder about "which items should be expanded" among the input items. Also, for the system side, it is possible to prevent the situation where the user expands input items that are not very important when creating a summary, causing the step of "determining that sufficient information for generating a summary of the work history has not been input (i.e., Step S140; insufficient)" in FIG. 2 from looping.

[0058] FIG. 7 is a diagram showing an example of the screen configuration of the input promotion screen according to the first embodiment. The figure shows a display screen D4 which is an example of the resume input promotion screen. The display screen D4 includes, as screen components, reference numerals D401, D402, and D403.

[0059] Reference numeral D401 indicates the progress of resume input. In the example shown, it is shown to have eight levels of input information. Also, currently, it shows the screen for inputting regarding the first level, work history and academic background. Although it is preferable that as much information as possible be input by the user, the summary generation device 10 only needs to be able to obtain sufficient information for generating a summary.

[0060] Reference numeral D402 shows a part of the input items regarding the work history and academic background which are the first level. Specifically, in the example shown, it shows the screen for inputting the current annual income. The user selects the range indicating the current annual income from the selected ranges. In the example shown, since "less than 2 million yen" is highlighted, it is in the state where "less than 2 million yen" is selected.

[0061] The symbol D403 is the button to proceed to the next step. The user expands the resume by repeating the input to the displayed items and the operation of the proceed button.

[0062] FIG. 8 is a diagram showing an example of the screen configuration of the AI summary generation execution screen after the information according to the first embodiment is enriched. When the expansion of the resume is completed, a display screen D5 as shown in the figure is displayed. The display screen D5 includes symbols D501, D502, D503, and D504 as screen components. The display screen D5 is similar to the display screen D1, but is different from the display screen D1 in that it further includes the symbol D504. The words "The resume has been enriched" are displayed on the symbol D504. That is, the user can recognize that the resume has been enriched by visually recognizing the display of the symbol D504.

[0063] (Step S170) Returning to FIG. 3, the summary generation device 10 receives an AI summary generation execution operation from the user. The AI summary generation execution operation is received, for example, when the user presses the AI summary generation execution button of the symbol D502 described above. When the summary generation device 10 receives an AI summary generation execution operation from the user, it may present the precautions for use as shown in the above-described display screen D2 and request the user's consent again.

[0064] (Step S180) When the AI summary generation execution operation is received, the resume information is input into the learning model. The learning model may be an AI based on a learning model such as a language model such as a Transformer including GPT (including Generative Pretrained Transformer, GPT-1, GPT-2, GPT-3). Further, the learning model may be an AI based on a multimodal model (for example, including GPT-4, etc.) that can simultaneously understand and process multiple types of data such as text, images, and voices in addition to the above-described language model. The learning model uses the resume information as input information, makes inferences, and outputs a summary of the work history resume.

[0065] (Step S190) When the summary of the work history document is output as a result of the inference by the learning model, confirmation of the product by the user is performed.

[0066] FIG. 9 is a diagram showing an example of the screen configuration of the product confirmation screen according to the first embodiment. When the summary of the work history document is output as a result of the inference by the learning model, the summary generation device 10 causes the user terminal device 30 to display a display screen D6 as shown in the figure, thereby performing confirmation of the product by the user. The display screen D6 has a symbol D601, a symbol D602, and a symbol D603 as screen components.

[0067] The symbol D601 describes the summary of the work history document. Specifically, the symbol D601 describes, "I have been working as a full-time employee in the personnel / recruitment department at ABCD Co., Ltd. since November 2018. The recruitment targets I am in charge of are sales, planning / marketing, sales, etc. I am also in charge of recruitment interviews such as new graduate interviews and mid-career interviews, and also in charge of personnel system planning for a scale of 1 to 20 people. As personnel work, I have experience in tasks such as formulating personnel plans, calculating salaries, handling labor issues, and planning personnel systems. Also, as a management position, I led and oversaw 5 to 9 subordinates."

[0068] The symbol D602 is a copy button. By operating the copy button, the user can copy the summary of the work history document described in the symbol D601 as text data. The copied text data can be used as appropriate according to the user's convenience.

[0069] The symbol D603 is an insertion button. By operating the insertion button, the user can insert the text data displayed in the symbol D601 into the symbol D501, which is the configuration that the display screen D5 has. When the insertion button is operated, a display screen in a state where the summary of the work history document is inserted is displayed for the symbol D501.

[0070] (Step S200) The user checks the summary of the work history generated by the AI summary generation process, makes corrections as necessary, and saves the data.

[0071] FIG. 10 is a diagram showing an example of the screen configuration of the product storage screen according to the first embodiment. The display screen D7 shown in this figure is an example of a display screen in a state where the summary of the work history is inserted for reference sign D501. The display screen D7 has reference signs D701, D702, and D703 as components of the screen. The display screen D7 is similar to the display screen D5, but is different from the display screen D5 in that the summary of the work history generated by the AI summary generation process is displayed at reference sign D701.

[0072] The user edits the text displayed at reference sign D701, and after the editing is done, operates reference sign D703 to save the summary of the work history. Note that it is not essential for the summary generation device 10 to have the editing step, and it may be omitted as appropriate, or the edited content may be automatically saved without the user performing a save operation.

[0073] FIG. 11 is a functional configuration diagram showing an example of the functional configuration of the summary generation device according to the first embodiment. With reference to this figure, an example of the functional configuration of the summary generation device 10 for realizing the functions as described above will be described. The summary generation device 10 operates by executing a summary generation program. The summary generation device 10 includes a summary operation reception unit 11, an input information acquisition unit 12, a determination unit 13, an information input request unit 14, and a summary generation unit 15 as functional configurations.

[0074] The summary operation reception unit 11 receives an operation from the user to generate a summary of information (for example, work history information) used for personnel recruitment. The summary operation reception unit 11 receives an operation to generate a summary, for example, when the user operates the button D102 that the above-described display screen D1 has. When the summary operation reception unit 11 receives an operation to generate a summary, it instructs the determination unit 13 to start the summary.

[0075] The input information acquisition unit 12 acquires primary information. The primary information is information used for generating a summary and is information that has already been input by the user. Specifically, the primary information is items as described with reference to FIG. 2. Here, the primary information includes required input items and optional input items. A required input item is an input item for which it is essential that information be input to generate a summary. An optional input item is an input item for which it is not essential that information be input to generate a summary. The required input items include at least input items related to education history or work history.

[0076] Furthermore, the primary information includes selection items and natural language input items. A selection item is an item for which an arbitrary option is selected by the user from a plurality of preset options. For example, the selection items are items such as option 5b, option 5d, and option 5f as described with reference to FIG. 2(B). A natural language input item is an item into which natural language is input by the user. For example, the natural language input item is an item such as the affiliated company input field 4b as described with reference to FIG. 2(A).

[0077] The determination unit 13 determines whether sufficient information for generating a summary has already been input based on the primary information acquired by the input information acquisition unit 12. Specifically, the determination unit 13 refers to the primary information acquired by the input information acquisition unit 12 and may determine that sufficient information for generating a summary has not been input if information has not been input for the required input items. Note that the determination method of the determination unit 13 is not limited to this example, and other algorithms (such as machine learning algorithms, etc.) may be used to determine whether sufficient information for generating a summary has already been input.

[0078] When the determination unit 13 determines that insufficient information has been input to generate a summary, the information input request unit 14 requests the user to input information. Note that the information input request unit 14 may request the user to input all of the primary information, or may request the user to input only the above-described essential input items (or input items including the essential input items). Based on the request made by the information input request unit 14, the user inputs information in addition to the primary information. The information generated by the additional input may be described as secondary information.

[0079] The summary generation unit 15 generates a summary based on the secondary information generated by the user's additional input. The summary generation unit 15 may generate a summary by machine learning using a language model such as a transformer including GPT. Note that it is preferable for the summary generation unit 15 to generate a summary based on the selection items among the selection items and natural language input items included in the secondary information (or the primary information before the addition). For example, when the summary generation unit 15 generates a summary by machine learning, if inferences are made based on the natural language freely input by the user, unintended results may be output. Therefore, the summary generation unit 15 can generate a highly accurate summary by making inferences based on the selection input items.

[0080] FIG. 12 is a block diagram showing an example of the internal configuration of the summary generation device according to the first embodiment. At least some functions of the summary generation device 10 can be realized using a computer. As shown in the figure, the computer includes a central processing unit 901, a RAM 902, an input / output port 903, input / output devices 904 and 905, etc., and a bus 906. The computer itself can be realized using existing technologies. The central processing unit 901 executes instructions included in a program read from the RAM 902 or the like. The central processing unit 901 writes data to the RAM 902, reads data from the RAM 902, and performs arithmetic operations and logical operations according to each instruction. The RAM 902 stores data and programs. Each element included in the RAM 902 has an address and can be accessed using the address. Note that RAM is an abbreviation for "Random Access Memory". The input / output port 903 is a port for the central processing unit 901 to exchange data with external input / output devices and the like. The input / output devices 904 and 905 are input / output devices. The input / output devices 904 and 905 exchange data with the central processing unit 901 via the input / output port 903. The bus 906 is a common communication path used inside the computer. For example, the central processing unit 901 reads and writes data in the RAM 902 via the bus 906. Also, for example, the central processing unit 901 accesses the input / output port via the bus 906. Also, all or part of each functional unit provided in the summary generation device 10 may be realized using hardware such as an ASIC, a PLD, or an FPGA. Also, all or part of each functional unit may be realized by a combination of software and hardware.

[0081] [Summary of the First Embodiment] According to the above-described embodiment, the summary generation device 10 includes a summary operation reception unit 11 to receive from a user an operation for generating a summary of information (e.g., a resume) used for personnel recruitment, and includes an input information acquisition unit 12 to acquire primary information that is information used for generating a summary and has already been input by the user. By including a determination unit 13, it is determined whether sufficient information for generating a summary has already been input based on the primary information acquired by the input information acquisition unit 12. By including an information input request unit 14, when the determination unit 13 determines that sufficient information for generating a summary has not been input, the user is requested to input information. By including a summary generation unit 15, a summary is generated based on secondary information generated by the user adding and inputting information to the primary information based on the request made by the information input request unit 14.

[0082] Here, according to the prior art, the creation of a resume has been assisted by having applicants and the like input information into a pre-prepared resume format. However, the amount of information input into the resume format may vary depending on the applicant and the like, and it is preferable to use resumes with approximately the same amount of information for users and the like to make a fair primary judgment on employment eligibility. According to this embodiment, a summary of the resume can be suitably generated based on the primary information that has already been input. Also, the amount of the summary generated by the summary generation device 10 is approximately the same regardless of the type of primary information. Therefore, according to this embodiment, users and the like who check the summary can make a fair primary judgment on employment eligibility.

[0083] Also, according to the above-described embodiment, the primary information includes mandatory input items and optional input items. The determination unit 13 refers to the primary information acquired by the input information acquisition unit 12 and determines that sufficient information for generating a summary has not been input when information has not been input into the mandatory input items. Therefore, the summary generation device 10 can easily determine whether the amount of primary information is sufficient.

[0084] Further, according to the above-described embodiment, the mandatory input items at least include input items related to educational background or work experience. That is, the summary generation device 10 generates a summary using educational background or work experience as mandatory input items. Therefore, according to the present embodiment, a summary including at least information about educational background or work experience can be generated. Educational background and work experience are important for the judgment of employment eligibility. Therefore, according to the present embodiment, users who check the summary can fairly make a primary judgment on employment eligibility.

[0085] Further, according to the above-described embodiment, the primary information includes a selection item for selecting an arbitrary option from a plurality of preset options and a natural language input item for inputting natural language. The summary generation unit 15 generates a summary based on the selection item. That is, according to the present embodiment, although there is a field for arbitrarily inputting natural language, it is not used for generating the summary, and the summary is generated based on the selection item. Therefore, according to the present embodiment, the risk of generating an unintended summary can be avoided.

[0086] [Second Embodiment] Next, with reference to FIGS. 13 and 14, a second embodiment will be described. The second embodiment is different from the first embodiment in that the data input to the learning model until the summary is generated is shaped to generate a summary with higher accuracy. In the description of the second embodiment, the description of matters already described in the first embodiment may be omitted.

[0087] FIG. 13 is a flowchart showing an example of the operation flow of the summary generation device according to the second embodiment. With reference to this figure, a series of processes performed by the summary generation device 10A according to the second embodiment will be described. In each step described with reference to this figure, the configuration of the display screen displayed on the user terminal device 30 may be the same as that already described in the first embodiment.

[0088] (Step S310) First, the summary generation device 10A receives an AI summary generation execution operation from the user. Step S310 corresponds to Step S120 in the first embodiment. When the summary generation device 10A receives an AI summary generation execution operation from the user, the process proceeds to Step S320.

[0089] (Step S320) Next, the summary generation device 10A checks the resume information (primary information) previously input by the user, and extracts information used for generating the summary, such as the appeal points, work history, education background, and expectations. It is preferable that what kind of information is to be extracted is predetermined. The summary generation device 10A extracts information about predetermined items (for example, the user's appeal points).

[0090] (Step S330) Next, the primary information is formatted into information for inputting to the learning model. The formatting process may be performed, for example, by text processing. In the formatting process, for example, the user's appeal points may be moved to the front and emphasized by attaching specific codes.

[0091] (Step S340) Next, the resume information after formatting is input into the learning model. The learning model makes inferences using the resume information after formatting as input information, and outputs a summary of the work history resume.

[0092] (Step S350) When a summary of the work history resume is output as a result of the inference by the learning model, the user confirms the generated product.

[0093] (Step S360) The user checks the summary of the work history resume generated by the AI summary generation process, makes corrections as necessary, and saves the data.

[0094] FIG. 14 is a functional configuration diagram showing an example of the functional configuration of the summary generation device according to the second embodiment. With reference to this figure, an example of the functional configuration of the summary generation device 10A for realizing the functions as described with reference to FIG. 13 will be described. The summary generation device 10A includes a summary operation reception unit 11, an input information acquisition unit 12, an extraction unit 161, a preprocessing unit 162, and a summary generation unit 15. That is, the summary generation device 10A is different from the summary generation device 10 in that it includes an extraction unit 161 and a preprocessing unit 162 instead of the information input request unit 14. Also, the summary generation device 10A is different from the summary generation device 10 in that it includes a summary generation unit 15A instead of the summary generation unit 15. In the description of the summary generation device 10A, the same components as those of the summary generation device 10 may be denoted by the same reference numerals and the description may be omitted.

[0095] The extraction unit 161 extracts the appeal points included in the primary information pre-input by the user. Here, it is preferable that the primary information includes a plurality of items and information on whether or not each item has been selected as an appeal point by the user. Whether or not it is an appeal point is selected by the user using a display screen as shown in FIG. 2(C). The extraction unit 161 refers to the primary information pre-input by the user and extracts the items selected as appeal points by the user as appeal points.

[0096] Here, when many appeal points are selected by the user, it is not easy to generate a summary that emphasizes the appeal points. Therefore, it is preferable to set an upper limit on the number of items that can be selected as appeal points based on the primary information. As the upper limit number, about 5 is preferably set. Also, as a numerical limit on the number of appeal points, a lower limit number or a reference number may be determined.

[0097] In addition, in this embodiment, the appeal point may be further selected from among the items selected as skills by the user. In other words, it can also be said that in this embodiment, the method for selecting primary information has a hierarchical structure. As the first layer, the skills possessed by the user are selected, and only the skills selected in the second layer are displayed, and the structure is such that the appeal point is selected from among the displayed options. Also, it can be said that the appeal point is further selected from among the options selected in the selection items.

[0098] The preprocessing unit 162 generates information in a predetermined data format based on the primary information acquired by the input information acquisition unit 12 and the appeal point extracted by the extraction unit 161. The predetermined data format may be, for example, a text data format, and for example, it may be a format in which the appeal point is described at the front.

[0099] The summary generation unit 15A takes the information generated by the preprocessing unit 162 as input and generates a summary by performing inference using a machine learning model that has been pre-trained. The summary generation unit 15A may generate a summary by machine learning using a language model such as a transformer including GPT. Here, it is preferable that the machine learning model used by the summary generation unit 15A for generating the summary is pre-trained for each category. The category may be a classification used when the user selects items suitable for himself or herself, such as academic background, work experience, skills, etc.

[0100] [Summary of the Second Embodiment] According to the above-described embodiments, the summary generation device 10A includes a summary operation reception unit 11 to receive an operation from a user regarding generating a summary of information used for human resource recruitment, includes an input information acquisition unit 12 to acquire primary information which is information used for generating a summary and has already been input by the user, includes an extraction unit 161 to extract appeal points included in the primary information, includes a preprocessing unit 162 to generate information in a predetermined data format based on the primary information acquired by the input information acquisition unit 12 and the appeal points extracted by the extraction unit 161, and includes a summary generation unit 15A to generate a summary by performing inference using a machine learning model that has been pre-trained with the information generated by the preprocessing unit 162 as input. By adopting such a configuration, it is possible to generate an accurate summary of a resume based on the already input primary information. According to this embodiment, since an accurate summary of a resume can be generated, the user can shorten the time required for modifying the summary. In addition, the user can generate a summary of a resume that accurately reflects the points the user wants to appeal.

[0101] Further, according to the above-described embodiments, the primary information includes a plurality of items and includes information on whether each item has been selected as an appeal point by the user. The extraction unit 161 refers to the primary information and extracts items selected as appeal points by the user as appeal points, and there is an upper limit to the number of items that can be selected as appeal points based on the primary information. Therefore, according to this embodiment, it is possible to prevent a summary in which the points to be appealed become ambiguous from being created by setting a large number of appeal points.

[0102] Further, according to the above-described embodiment, the primary information includes a selection item in which an arbitrary option is selected from a plurality of options preset for each category, and the appeal point is further selected from the options selected in the selection item. In other words, it can also be said that the appeal point is selected in two stages. In the first stage, the user's skill is selected, and in the second stage, the appeal point is selected. According to this embodiment adopting such a configuration, the user can easily generate a summary of the work history document that reflects their own appeal points.

[0103] Further, according to the above-described embodiment, the machine learning model used by the summary generation unit 15A for inference is learned for each category. Therefore, according to this embodiment, more accurate inference can be performed, and a more accurate summary of the work history document can be generated.

[0104] [Third Embodiment] Next, the third embodiment will be described with reference to FIGS. 15 to 17. The third embodiment is different from the first and second embodiments in that, as a result of generating a summary, the summary is generated (re-generated) again according to the user's feedback. In the description of the third embodiment, the description of matters already described in the first or second embodiment may be omitted.

[0105] FIG. 15 is a flowchart showing an example of the operation flow of the summary generation apparatus according to the third embodiment. With reference to this figure, a series of processes performed by the summary generation apparatus 10B according to the third embodiment will be described. In each step described with reference to this figure, the configuration of the display screen displayed on the user terminal device 30 may include the same ones as already described in the first or second embodiment. A configuration example of the display screen specific to the third embodiment will be separately described with reference to the drawings.

[0106] (Step S510) First, the abstract generation device 10B receives an AI abstract generation execution operation from the user. Step S510 corresponds to Step S120 in the first embodiment. When the abstract generation device 10B receives an AI abstract generation execution operation from the user, the process proceeds to Step S520.

[0107] (Step S520) When an AI abstract generation execution operation is received, the resume information is input into the learning model. The learning model may be an AI equipped with a learning model such as a language model like a Transformer including GPT. The learning model takes the resume information as input information, makes inferences, and outputs a summary of the work history resume.

[0108] (Step S530) When a summary of the work history resume is output as a result of the inference by the learning model, confirmation of the product by the user is performed.

[0109] (Step S540) Here, in the third embodiment, it is different from the first and second embodiments in that there is a step of providing feedback to the product (which may be simply referred to as FB). In the step of providing feedback to the product, the abstract generation device 10B obtains feedback from the user.

[0110] FIG. 16 is a diagram showing an example of the screen configuration of the feedback screen according to the third embodiment. The display screen D8 shown in the figure is the screen displayed to the user in the step of providing feedback to the product. The display screen D8 includes reference numeral D801, reference numeral D802, and reference numeral D803 as components of the screen.

[0111] Symbols D801 and D802 are feedback information acquisition buttons. Specifically, symbol D801 is the positive feedback information acquisition button, and symbol D802 is the negative feedback information acquisition button. By selecting either symbol D801 or D802, the user provides feedback on whether the summary of the displayed resume is good (positive) or not good (negative). After the user selects either symbol D801 or D802, the user operates the submission button shown by symbol D803 to submit feedback information to the summary generation device 10B.

[0112] Note that the feedback information is not limited to information on whether the summary of the displayed resume is good or not, and may include other detailed information. Other detailed information may be, for example, "the article structure is difficult to understand", "there are problems with expression and grammar", "the summary is too long", "the summary is too short", "skills and achievements are not well presented", "there is incorrect information", "there are parts where information is lacking", "others", etc., or may be indicated by scoring like "5 - level display" with numbers or the number of "stars", etc. Also, the summary generation device 10B may present these options to the user as detailed information and acquire feedback information based on the user's selection.

[0113] (Step S550) Returning to FIG. 15, the summary generation device 10B determines whether the feedback information is positive. If the feedback information is positive (i.e., Step S550; YES), the summary generation device 10B proceeds to step S580. If the feedback information is negative (i.e., Step S550; NO), the summary generation device 10B proceeds to step S560.

[0114] (Step S560) If the feedback information is negative, the summary generation device 10B performs inference using the learning model again. In the second inference process, the inference may be performed based on the "other information" obtained as the feedback information. Note that in the second inference process, the inference may be performed using a different learning model based on the "other information" obtained as the feedback information.

[0115] (Step S570) Next, the summary generation device 10B presents the summary obtained by the second inference process to the user.

[0116] (Step S580) When the summary of the work history document is output as a result of the inference by the learning model in the second inference process, the user checks the generated product.

[0117] (Step S590) The user checks the summary of the work history document generated by the AI summary generation process, makes corrections if necessary, and saves the data.

[0118] FIG. 17 is a functional configuration diagram showing an example of the functional configuration of the summary generation device according to the third embodiment. With reference to this figure, an example of the functional configuration of the summary generation device 10B for realizing the functions as described with reference to FIGS. 15 and 116 will be described. The summary generation device 10B includes a summary operation reception unit 11, an input information acquisition unit 12, a feedback information acquisition unit 18, a first summary generation unit 151, a second summary generation unit 152, and a presentation unit 17. The summary generation device 10B is different from the summary generation device 10 in that it includes a feedback information acquisition unit 18, a first summary generation unit 151, a second summary generation unit 152, and a presentation unit 17. In the description of the summary generation device 10B, the same components as those of the summary generation device 10 may be denoted by the same reference numerals and the description may be omitted.

[0119] The first summary generation unit 151 acquires information indicating the start of summarization from the summarization operation reception unit 11, and acquires the input information from the input information acquisition unit 12. Triggered by the acquisition of the information indicating the start of summarization, the first summary generation unit 151 uses the primary information acquired by the input information acquisition unit 12 as input and performs inference using a machine learning model that has been pre-trained. The first summary generation unit 151 generates a summary by performing inference using the machine learning model. The first summary generation unit 151 provides the result of the generated summary to the presentation unit 17 as first summary information.

[0120] The presentation unit 17 presents the summary generated by the first summary generation unit 151 to the user.

[0121] The feedback information acquisition unit 18 acquires feedback information about the summary presented by the presentation unit 17 from the user. The feedback information acquired by the feedback information acquisition unit 18 includes at least information on whether the user is satisfied with the summary presented by the presentation unit 17. Further, when the user is not satisfied, information about the reason for dissatisfaction may also be included. The information about the reason for dissatisfaction of the user may be information selected from options or text information input by the user.

[0122] The second summary generation unit 152 acquires input information from the input information acquisition unit 12 and acquires feedback information from the feedback information acquisition unit 18. The second summary generation unit 152 uses, as inputs, the primary information acquired by the input information acquisition unit 12 and the feedback information acquired by the feedback information acquisition unit 18, and performs inference using a machine learning model that has been pre-trained to generate a summary. The second summary generation unit 152 generates a summary by performing inference using the machine learning model. The second summary generation unit 152 provides the result of the generated summary to the presentation unit 17 as second summary information. Note that the machine learning model used by the first summary generation unit 151 for inference and the machine learning model used by the second summary generation unit 152 for inference may be the same as each other or different from each other.

[0123] Note that when the feedback information acquisition unit 18 acquires information about the reason why the user is not satisfied, the second summary generation unit 152 may use, as inputs, the primary information acquired by the input information acquisition unit 12 and the reason why the user is not satisfied included in the feedback information acquired by the feedback information acquisition unit 18, and perform inference using a machine learning model that has been pre-trained to generate a summary. In this case, the machine learning model used by the second summary generation unit 152 for inference may be pre-trained according to the reason why the user is not satisfied.

[0124] For example, when feedback such as "The text structure was difficult to understand" is obtained, an inference may be made based on a machine learning model that excels in creating an easy-to-understand text structure. Also, in advance, a machine learning model characterized by outputting a long summary and a machine learning model characterized by outputting a short summary may be prepared, and depending on whether the feedback from the user is that the summary is "too long" or "too short", a suitable machine learning model may be selected for inference. In this case, the second summary generation unit 152 may also generate a summary by selecting machine learning models that have been pre-trained based on different teacher data respectively based on the reasons for the user's dissatisfaction included in the feedback information obtained by the feedback information acquisition unit 18, and performing inference using the selected machine learning models.

[0125] Also, when feedback such as "Skills and achievements were not well presented", "There was incorrect information", or "There were parts where information was lacking" is obtained, the user may supplement the information again, and after the second supplementation, summary generation and presentation may be performed for the third and subsequent times.

[0126] [Summary of the Third Embodiment] According to the above-described embodiment, summarization device 10B includes a summarization operation reception unit 11 to receive from a user an operation for generating a summary of information used for personnel recruitment, includes an input information acquisition unit 12 to acquire primary information, which is information used for generating a summary and has already been input by the user, includes a first summary generation unit 151 to generate a summary by performing inference using a machine learning model that has been pre-trained with the primary information acquired by the input information acquisition unit 12 as input, includes a presentation unit 17 to present the summary generated by the first summary generation unit 151, includes a feedback information acquisition unit 18 to acquire from the user feedback information about the summary presented by the presentation unit 17, and includes a second summary generation unit 152 to generate a summary by performing inference using a machine learning model that has been pre-trained with the primary information acquired by the input information acquisition unit 12 and the feedback information acquired by the feedback information acquisition unit 18 as input. By adopting such a configuration, it is possible to generate a high-precision summary of a resume based on feedback from the user. According to this embodiment, since a high-precision summary of a resume can be generated, the user can shorten the time required to revise the summary. Further, the user can generate a summary of a resume that reflects the points the user wants to emphasize.

[0127] Also, according to the above-described embodiment, the feedback information at least includes information on whether the user is satisfied with the summary presented by the presentation unit 17. When the user is not satisfied, the information on the reason for dissatisfaction is further included. Here, in services such as GPT, due to additional learning, the inference result may change depending on the timing of inference. Therefore, when the summary generation device 10B performs inference using a service such as GPT, it is assumed that the inference result of the summary generation device 10B will change depending on the timing of inference. Therefore, it is crucial to detect changes quickly. According to this embodiment, in order to obtain feedback from the user, based on the feedback, changes in the learning model can be detected.

[0128] Also, according to the above-described embodiment, the second summary generation unit 152 uses, as inputs, the primary information acquired by the input information acquisition unit 12 and the reason for the user's dissatisfaction included in the feedback information acquired by the feedback information acquisition unit 18, and performs inference using a machine learning model that has been pre-trained to generate a summary. That is, the summary generation device 10B performs inference based on the details of the feedback from the user. Therefore, according to this embodiment, a more accurate summary of the resume can be generated.

[0129] Also, according to the above-described embodiment, the second summary generation unit 152 selects machine learning models that have been pre-trained respectively based on different teacher data based on the reason for the user's dissatisfaction included in the feedback information acquired by the feedback information acquisition unit 18, and generates a summary by performing inference using the selected machine learning models. According to this embodiment, since the summary generation device 10B performs inference using a learning model corresponding to the type of feedback, a more accurate summary of the resume can be generated.

[0130] Note that, the entire functions or a part of each part of the apparatuses included in the summary generation device 10, the summary generation device 10A, and the summary generation device 10B described above may be realized by recording a program for realizing these functions on a computer-readable recording medium, reading the program recorded on this recording medium into a computer system, and executing it. Here, the "computer system" is assumed to include hardware such as an OS and peripheral devices.

[0131] Further, the "computer-readable recording medium" refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, a CD-ROM, etc., and a storage unit such as a hard disk built in a computer system. Furthermore, the "computer-readable recording medium" also includes something that dynamically holds a program for a short time like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line, and something that holds a program for a certain period of time like a volatile memory inside a computer system that becomes a server or a client in that case. Also, the above program may be for realizing a part of the functions described above, and furthermore, it may be something that can be realized in combination with a program already recorded in the computer system for realizing the functions described above.

[0132] As described above, the embodiments of the present invention have been explained, but the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention.

Explanation of Reference Numerals

[0133] 1... System, 10... Summary generation device, 30... User terminal device, 11... Summary operation reception unit, 12... Input information acquisition unit, 13... Judgment unit, 14... Information input request unit, 15... Summary generation unit, 161... Extraction unit, 162... Preprocessing unit, 17... Presentation unit, 18... Feedback information acquisition unit, 151... First summary generation unit, 152... Second summary generation unit

Claims

1. A summary operation reception unit that receives an operation for generating a summary of information used for personnel recruitment from a user; An input information acquisition unit that acquires primary information which is information used for generating the summary and has already been input by the user; A first summary generation unit that generates a summary by performing inference using a machine learning model that has been pre-trained, with the primary information acquired by the input information acquisition unit as the input; A presentation unit that presents the summary generated by the first summary generation unit; A feedback information acquisition unit that acquires feedback information about the summary presented by the presentation unit from the user; A second summary generation unit that generates a summary by performing inference using a machine learning model that has been pre-trained, with the primary information acquired by the input information acquisition unit and the feedback information acquired by the feedback information acquisition unit as the input; A summary generation device comprising the above.

2. The feedback information includes at least information on whether the user is satisfied with the summary presented by the presentation unit. When the user is not satisfied, the feedback information further includes information on the reason for dissatisfaction. The summary generation device according to Claim 1. The summary generation device according to Claim 1.

3. The second summary generation unit generates a summary by performing inference using a machine learning model that has been pre-trained, with the primary information acquired by the input information acquisition unit and the reason for dissatisfaction of the user included in the feedback information acquired by the feedback information acquisition unit as the input. The summary generation device according to Claim 2. The summary generation device according to Claim 2.

4. Based on the reason for dissatisfaction of the user included in the feedback information acquired by the feedback information acquisition unit, the second summary generation unit selects machine learning models that have been pre-trained based on different teacher data respectively, and generates a summary by performing inference using the selected machine learning models. The summary generation device according to Claim 3. The summary generation device according to Claim 3.

5. A summary operation reception step of receiving an operation for generating a summary of information used for personnel recruitment from a user; An input information acquisition step of acquiring primary information which is information used for generating the summary and has already been input by the user; A first summary generation step of generating a summary by using, as input, the primary information obtained in the input information acquisition step and performing inference using a machine learning model that has been pre-trained; A presentation step of presenting the summary generated in the first summary generation step; A feedback information acquisition step of acquiring, from the user, feedback information about the summary presented in the presentation step; A second summary generation step of generating a summary by using, as input, the primary information obtained in the input information acquisition step and the feedback information obtained in the feedback information acquisition step and performing inference using a machine learning model that has been pre-trained; A summary generation method comprising the above.

6. On a computer, A summary operation reception step of receiving, from a user, an operation for generating a summary of information used for personnel recruitment; An input information acquisition step of acquiring primary information, which is information used for generating the summary and has already been input by the user; A first summary generation step of generating a summary by using, as input, the primary information obtained in the input information acquisition step and performing inference using a machine learning model that has been pre-trained; A presentation step of presenting the summary generated in the first summary generation step; A feedback information acquisition step of acquiring, from the user, feedback information about the summary presented in the presentation step; A second summary generation step of generating a summary by using, as input, the primary information obtained in the input information acquisition step and the feedback information obtained in the feedback information acquisition step and performing inference using a machine learning model that has been pre-trained; A program for causing the above to be executed.

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