Article generation method and device, equipment and storage medium

Abstract

The present disclosure provides an article generation method and device, equipment and storage medium, relates to the technical field of artificial intelligence, in particular to the technical field of large model, agent and text processing. The specific implementation scheme is: obtaining a writing request, identifying the writing request to obtain a writing requirement; generating a draft outline and available materials according to the writing requirement; rewriting the draft outline by using the available materials to obtain a target article outline; and generating a target article according to the target article outline. Through the above technical scheme, the article generation quality can be improved.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the field of artificial intelligence, in particular to the fields of large models and text processing. BACKGROUND

[0002] With the explosive development of artificial intelligence large model technology, the trend application and product around the large model are attracting more and more attention. Among many large model application scenarios, intelligent writing is particularly prominent. Through inputting simple instructions, a large model can generate a generally good article draft, which is very exciting. But after a lot of trial, many intelligent writing products are difficult to generate long articles, and the generated content often lacks depth and substance. Therefore, there is an urgent need for an efficient long article generation method. SUMMARY

[0003] The present disclosure provides an article generation method, device, equipment and storage medium.

[0004] According to an aspect of the present disclosure, an article generation method is provided, which comprises:

[0005] Obtaining a writing request, identifying the writing request to obtain a writing requirement;

[0006] Generating a draft outline and available materials according to the writing requirement, respectively;

[0007] Rewriting the draft outline using the available materials to obtain a target article outline;

[0008] Generating a target article according to the target article outline.

[0009] According to another aspect of the present disclosure, an article generation device is provided, which comprises:

[0010] A writing requirement determination module for obtaining a writing request, identifying the writing request to obtain a writing requirement;

[0011] An outline and material generation module for generating a draft outline and available materials according to the writing requirement, respectively;

[0012] A target outline determination module for rewriting the draft outline using the available materials to obtain a target article outline;

[0013] A target article generation module for generating a target article according to the target article outline.

[0014] According to another aspect of the present disclosure, an electronic device is provided, which comprises:

[0015] At least one processor; and

[0016] a memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the article generation method described in any embodiment of the present disclosure.

[0018] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable a computer to execute the article generation method described in any embodiment of the present disclosure.

[0019] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the article generation method described in any embodiment of the present disclosure.

[0020] According to the technology disclosed in the present invention, the quality of article generation can be improved.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0023] Figure 1 is a flowchart of a method for generating an article according to an embodiment of the present disclosure;

[0024] Figure 2 is a flowchart of another article generation method provided according to an embodiment of the present disclosure;

[0025] Figure 3 is a flowchart of another article generation method provided according to an embodiment of the present disclosure;

[0026] Figure 4 is a flowchart of another article generation method provided according to an embodiment of the present disclosure;

[0027] Figure 5 1 is a schematic structural diagram of an article generating device provided according to an embodiment of the present disclosure;

[0028] Figure 6 It is a block diagram of an electronic device used to implement the article generation method according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0029] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0030] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0031] In addition, it should be noted that the collection, storage, use, processing, transmission, provision and disclosure of writing request-related data involved in the technical solution of the present invention comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0032] Figure 1 It is a flowchart of an article generation method provided according to an embodiment of the present disclosure. This method is applicable to situations in which article generation is performed, and is particularly applicable to situations in which long articles are generated in more professional writing scenarios such as bidding documents, architectural plans, and research reports. Specifically, it is applicable to situations in which the text generated by a large model is not long enough and has low quality. This method can be executed by an article generation device, which can be implemented in software and / or hardware, and can be integrated into an electronic device that carries the article generation function, such as a server. It should be noted that the article generation method disclosed in the present disclosure can be executed by an intelligent agent based on a large model, wherein an intelligent agent refers to a computer program based on a large language model, which has planning and thinking capabilities, memory capabilities, and the ability to use tool functions, and can independently complete a given task.

[0033] like Figure 1 As shown, the article generation method of this embodiment may include:

[0034] S101, obtaining a writing request, identifying the writing request, and obtaining writing requirements.

[0035] In this embodiment, the so-called writing request refers to a request for identifying writing requirements. The so-called writing requirements refer to requirements identified based on the writing request, which may include theme, style, word count, type, etc.

[0036] Specifically, the agent can obtain a writing request input by the demander. Using big model intent recognition, the agent can identify the writing request through the big model's request recognition to obtain the writing requirements. For example, if the writing request query = "Help me write a 10,000-word market research article on big models," the big model's request recognition can automatically identify the topic = "Big Model Market Research," the word count = "approximately 10,000 words," the type = "Market Research," and the style = "Formal."

[0037] It should be noted that in this disclosure, a large model refers to a machine learning model or a deep learning model with a large parameter scale and complexity. The so-called requirement recognition large model refers to a large model used for writing requirement recognition.

[0038] It should be further explained that after receiving a writing request, the intelligent agent can automatically plan and process to generate the target article.

[0039] S102, generate a draft outline and available materials according to writing requirements.

[0040] In this embodiment, the so-called draft outline refers to the article outline initially generated based on the writing request, and the so-called available materials refer to high-quality writing materials collected based on the writing request.

[0041] One option for generating a draft outline based on writing requirements is for the agent to search the text library based on the writing requirements, obtain related headings, and combine the related headings to generate a draft outline. Alternatively, the agent can input the writing requirements into a large model and output a draft outline.

[0042] Another optional way to generate usable materials based on writing requirements is that the intelligent agent uses the writing requirements as an index to retrieve related text content from a related text library as usable materials.

[0043] S103, using available materials, rewrite the draft outline to obtain the target article outline.

[0044] In this embodiment, the target article outline refers to the outline ultimately used for generating the target article.

[0045] Alternatively, the agent can determine content related to the draft outline from available resources and then refine and expand the draft outline into chapters to obtain the target article outline. For example, if the draft outline includes Chapter 1, Chapter 2, and Chapter 3, for Chapter 1, the agent can determine content related to the corresponding chapter title from available resources to generate Chapter 1.1, Chapter 1.2, and so on, to obtain the target article outline.

[0046] S104: Generate a target article according to the target article outline.

[0047] In this embodiment, the target article refers to a long article that is finally generated, for example, a long article with a word count exceeding a set word count value, wherein the set word count value may be a value of more than 10,000 words.

[0048] In an optional approach, the agent can generate the target article based on the target article outline based on the article generation model, where the article generation model can be a large model.

[0049] The technical solution provided by the disclosed embodiments obtains and identifies a writing request to obtain writing requirements. A draft outline and available materials are then generated based on the writing requirements. The draft outline is then rewritten using the available materials to obtain a target article outline. Finally, a target article is generated based on the target article outline. This technical solution significantly improves the authenticity, logical integrity, and novelty of the content of the final target article outline by re-updating the draft outline and then generating the target article outline. This results in a higher-quality target article.

[0050] On the basis of the above embodiment, as an optional method of the present disclosure, after generating the target article according to the target article outline, the method further includes: adding footnotes and / or references to the target article.

[0051] Specifically, the intelligent agent can use the large model to add footnotes to the content of the target article and / or add references based on the materials used in the article generation process.

[0052] It is understandable that by adding footnotes and references to the target article, the article can be traced back to its source, thereby improving the credibility of the article.

[0053] On the basis of the above embodiment, as another optional method of the present disclosure, after generating the target article according to the target article outline, the method further includes: generating pictures and / or charts for the target article.

[0054] Specifically, the agent can use the large model to generate images and / or charts for each chapter in the article. For example, the agent can use the large model to perform statistical comparative analysis on the chapter content and draw charts such as line graphs; and / or use the large model to identify object names in the chapter content and assign images to the object names based on image search.

[0055] It is understandable that by making pictures and diagrams for the target article, the article is made more beautiful and easier to understand.

[0056] Figure 2 This is a flow chart of another article generation method provided according to an embodiment of the present disclosure. Based on the above embodiment, this embodiment further optimizes "generating available materials according to writing requirements" and provides an optional solution. Figure 2 As shown, the article generation method provided in this embodiment includes:

[0057] S201: Acquire a writing request, identify the writing request, and obtain writing requirements.

[0058] S202, generate a draft outline according to writing requirements.

[0059] S203: Perform content retrieval according to writing requirements to obtain target content.

[0060] In this embodiment, the target content refers to excellent articles related to the writing requirements.

[0061] Specifically, the writing requirements can be used as an index to retrieve content from related article websites to obtain the target content. For example, the agent can use the large model to use the writing requirements as an index and use a search tool to retrieve content from related article websites to obtain the target content.

[0062] S204, generating editors with different perspectives based on the target content.

[0063] Specifically, the intelligent agent uses the big model for role-playing, that is, the big model generates editors with different perspectives based on the target content; for example, for a certain kind of food, different editors have different focuses on describing the food, such as historians pay more attention to the development and origin of the food, while gourmets pay more attention to the taste and cooking methods of the food.

[0064] S205, the editor asks circular questions from his / her perspective to obtain writing problems.

[0065] In this embodiment, writing questions refer to relevant questions corresponding to perspectives.

[0066] Specifically, the intelligent agent can perform role-playing through a large model. For each editor, it uses the editor's perspective to conduct cyclical question-answering to obtain writing problems.

[0067] S206: Split and rewrite the writing questions, and conduct content retrieval and summary to obtain usable materials.

[0068] The so-called available materials refer to the materials used to update the draft outline, which are presented in the form of questions and answers.

[0069] Specifically, the intelligent agent can split and rewrite writing problems through a large model, retrieve and summarize content from relevant content websites, and obtain usable materials.

[0070] S207, using available materials, rewrite the draft outline to obtain the target article outline.

[0071] It should be noted that the intelligent agent generates a draft outline through the big model first, which can ensure a good and coherent writing idea overall, but the outlines generated between the big models are often too empty. Therefore, the intelligent agent adopts the idea of ​​big model role-playing, allowing the big model to use retrieval tools to ask and answer questions by itself according to the writing requirements to collect high-quality writing materials, that is, usable materials, and then use the available materials combined with the draft outline to generate the final target article outline, so that the target article outline has significant improvements in many aspects such as content authenticity, logical integrity, and novelty.

[0072] S208: Generate a target article according to the target article outline.

[0073] The technical solution provided by the embodiments of the present disclosure generates a draft outline based on writing requirements, performs content retrieval based on the writing requirements to obtain target content, generates editors with different perspectives based on the target content, uses the editors to ask circular questions from their perspectives to obtain writing problems, splits and rewrites the writing problems, and performs content retrieval and summary to obtain available materials. The draft outline is rewritten using the available materials to obtain a target article outline, and then the target article is generated based on the target article outline. The above technical solution, through role-playing, collects and summarizes relevant content based on writing requirements, and can obtain high-quality writing materials, thereby providing support for the generation of the final article outline.

[0074] Figure 3 This is a flow chart of another article generation method provided according to an embodiment of the present disclosure. Based on the above embodiment, this embodiment further optimizes "generating a target article according to the target article outline" and provides an optional implementation scheme. Figure 3 As shown, the article generation method provided in this embodiment includes:

[0075] S301: Acquire a writing request, identify the writing request, and obtain writing requirements.

[0076] S302: Generate a draft outline and available materials according to writing requirements.

[0077] S303, using available materials, rewrite the draft outline to obtain the target article outline.

[0078] S304: extract the structure of the target article outline to obtain the outline hierarchical structure.

[0079] In this embodiment, the outline hierarchical structure refers to the hierarchical structure of the target article outline, including chapter nodes, such as a tree structure; wherein the root node is the article title, the second-level node is the second-level title, and so on.

[0080] Specifically, the intelligent agent can extract the structure of the target article outline according to the outline catalog of the target article outline to obtain the outline hierarchical structure.

[0081] S305: Generate an article according to the outline hierarchical structure to obtain a target article.

[0082] Specifically, the intelligent agent can traverse the chapter nodes in the outline hierarchy through the large model, generate the chapter content corresponding to each chapter node in parallel, and obtain the target article.

[0083] The technical solution provided by the disclosed embodiments obtains and identifies a writing request to obtain writing requirements. A draft outline and available materials are then generated based on the writing requirements. The draft outline is then rewritten using the available materials to obtain a target article outline. Finally, the target article outline is structurally extracted to obtain an outline hierarchy. The article is then generated based on the outline hierarchy to obtain the target article. This technical solution, by utilizing the outline hierarchy for article generation, ensures that the article generation process adheres to the outline, thereby ensuring article quality.

[0084] On the basis of the above embodiment, as an optional method of the present disclosure, an article is generated according to the outline hierarchical structure to obtain a target article, including: writing chapters from top to bottom according to the outline hierarchical structure to generate a first draft of the article; and checking the first draft of the article to obtain the target article.

[0085] Among them, the first draft of the article refers to the article content generated for the first time based on the outline hierarchical structure.

[0086] Specifically, the intelligent agent can adopt a large model to traverse the outline hierarchical structure, and generate each chapter article in parallel according to each chapter node from top to bottom. For example, it can traverse each root chapter node in parallel, and generate the corresponding chapter content of the second-level chapter nodes under each root chapter node in parallel to obtain the first draft of the article. After that, the first draft of the article can be checked, such as format check, word count check, etc., to obtain the target article.

[0087] It is understandable that by generating articles from top to bottom, it can be ensured that the article generation process follows the outline, making the article generation process overall logical and not unrealistic. Further, checking the first draft of the article can ensure that the generated article is more accurate.

[0088] Figure 4 This is a flow chart of another article generation method provided according to an embodiment of the present disclosure. Based on the above embodiment, this embodiment further optimizes "checking the draft of the article to obtain the target article" and provides an optional implementation plan. Figure 4 As shown, the article generation method of this embodiment may include:

[0089] S401: Obtain a writing request, identify the writing request, and obtain writing requirements.

[0090] S402: Generate a draft outline and available materials based on writing requirements.

[0091] S403, using available materials, rewrite the draft outline to obtain the target article outline.

[0092] S404: extract the structure of the target article outline to obtain the outline hierarchical structure.

[0093] S405: Write chapters from top to bottom according to the outline hierarchy to generate the first draft of the article.

[0094] S406, performing a merge check on the chapters in the first draft of the article to obtain the erroneous and missing chapters.

[0095] In this embodiment, the erroneous and / or omitted chapters refer to chapters that are written incorrectly and / or omitted, including erroneous chapters, omitted chapters, etc.

[0096] Specifically, the intelligent agent can compare the similarity between the chapter contents of each chapter in the first draft of the article and the outline of the target article, and obtain the erroneous chapters based on the comparison results. For example, for a certain chapter, if the chapter similarity is 0, then the chapter is the omitted chapter among the erroneous chapters; if the similarity in the comparison result of the chapter is less than the set threshold, then the chapter is the erroneous chapter.

[0097] S407, rewrite the erroneous and missing chapters to obtain the target article.

[0098] Specifically, the intelligent agent can use the large model to regenerate the content of the erroneous and missing chapters in the first draft of the article to obtain the target article.

[0099] It should be noted that in the process of generating the target article, since the large model may write too much, too little, or write incorrectly, fault tolerance and large model reflection mechanisms are added to merge and check the first draft of the article, so that the article generated by the large model strictly follows the article outline.

[0100] The technical solution provided by the embodiments of the present disclosure generates a draft outline and available materials based on writing requirements, then rewrites the draft outline using the available materials to obtain a target article outline, extracts the structure of the target article outline to obtain an outline hierarchy, and writes chapters from top to bottom based on the outline hierarchy to generate a first draft of the article. The chapters in the first draft of the article are then merged and checked to obtain erroneous or missing chapters, which are then rewritten to obtain the target article. The above technical solution ensures the accuracy of the target article by checking the first draft for errors and omissions to regenerate the corresponding chapter content, thereby improving the quality of the article.

[0101] On the basis of the above embodiment, as an optional method of the present disclosure, the first draft of the article is checked to obtain the target article, including: checking whether the chapters in the first draft of the article contain specified materials according to the outline hierarchical structure; if so, updating the first draft of the article according to the specified materials to obtain the target article.

[0102] The designated materials refer to special writing materials or writing requirements specified by the demander, which can be adopted. The demander refers to the party that has the demand to generate the article.

[0103] Specifically, the intelligent agent can check whether the specified material exists in each secondary chapter node in the outline hierarchy structure. If so, the specified material is used to replace the chapter content in the corresponding chapter node to obtain the target article.

[0104] It is understandable that by adding designated material checks, the personalized needs of the demanders for chapters can be met.

[0105] Based on the above embodiment, as an optional method of the present disclosure, the first draft of the article is checked to obtain the target article, including: checking the word count of the first draft of the article from bottom to top according to the outline hierarchical structure to obtain the check result; updating the first draft of the article according to the check result to obtain the target article.

[0106] Specifically, the agent can perform word count checks starting from the leaf chapter nodes in a bottom-up manner according to the outline hierarchy. For each leaf chapter node, if the chapter content corresponding to the leaf chapter node does not meet the word count requirement, the chapter content for the leaf chapter node is regenerated. For example, writing materials can be added, and the large model can regenerate the leaf chapter node until the word count requirement is met to obtain the target article. Increasing writing materials allows the large model to output more text keywords, thereby meeting the word count requirement.

[0107] It is understandable that by checking the word count of the first draft of the article from the bottom up, it can be ensured that the resulting article is a long article.

[0108] Figure 5 is a structural schematic diagram of an article generation device provided according to an embodiment of the present disclosure. The present disclosure is applicable to the case of how to generate an article, and is particularly applicable to the case of how to generate a long article in a more professional writing scenario such as a tender book, a construction scheme, a research report, and the like, and can be particularly applicable to the case of a long text generated by a large model being insufficient in length and low in quality. The device can be implemented in a software and / or hardware manner, and can be integrated in an electronic device carrying an article generation function, such as a server. As shown in Figure 5 , the article generation device 500 comprises:

[0109] a writing requirement determination module 501 configured to obtain a writing request, identify the writing request, and obtain a writing requirement;

[0110] an outline and material generation module 502 configured to generate a draft outline and available materials according to the writing requirement;

[0111] a target outline determination module 503 configured to rewrite the draft outline using the available materials to obtain a target article outline;

[0112] a target article generation module 504 configured to generate a target article according to the target article outline.

[0113] The technical solution provided by the embodiment of the present disclosure comprises the following steps: obtaining a writing request, identifying the writing request, and obtaining a writing requirement; then generating a draft outline and available materials according to the writing requirement; further rewriting the draft outline using the available materials to obtain a target article outline; and finally generating a target article according to the target article outline. The above technical solution renews the draft outline to obtain a target article outline, and then generates an article, so that the final target article outline is significantly improved in terms of content authenticity, logical integrity, and novelty, thereby making the obtained target article higher in quality.

[0114] Further, the outline and material generation module 502 is specifically configured to:

[0115] perform content retrieval according to the writing requirement to obtain target content;

[0116] generate editors with different perspectives according to the target content;

[0117] adopt the editors to cyclically ask questions in their perspectives to obtain writing questions;

[0118] split and rewrite the writing questions, and perform content retrieval and summary to obtain available materials.

[0119] Further, the target article generation module 504 comprises:

[0120] The hierarchical structure extraction unit is configured to perform structure extraction on the target article outline to obtain an outline hierarchical structure.

[0121] The target article generation unit is configured to perform article generation according to the outline hierarchical structure to obtain the target article.

[0122] Further, the target article generation unit comprises:

[0123] The article draft generation subunit is configured to perform chapter writing from top to bottom according to the outline hierarchical structure to generate an article draft.

[0124] The target article determination subunit is configured to perform checking on the article draft to obtain the target article.

[0125] Further, the target article determination subunit is specifically configured to:

[0126] perform merging checking on chapters in the article draft to obtain incorrect or missing chapters;

[0127] perform rewriting on the incorrect or missing chapters to obtain the target article.

[0128] Further, the target article determination subunit is specifically configured to:

[0129] perform checking on whether chapters in the article draft contain specified materials according to the outline hierarchical structure;

[0130] if yes, perform updating on the article draft according to the specified materials to obtain the target article.

[0131] Further, the target article determination subunit is specifically configured to:

[0132] perform word count checking on the target article from bottom to top according to the outline hierarchical structure to obtain a checking result;

[0133] perform updating on the article draft according to the checking result to obtain the target article.

[0134] Further, the device further comprises a footnote literature adding module configured to:

[0135] add footnotes and / or references to the target article after the target article is generated according to the target article outline.

[0136] Further, the device further comprises a figure generation module configured to:

[0137] generate pictures and / or charts for the target article after the target article is generated according to the target article outline.

[0138] According to an embodiment of the present disclosure, the present disclosure further provides an electronic device, a readable storage medium and a computer program product.

[0139] Figure 6 is a block diagram of an electronic device used to implement the article generation method of the embodiments of the present disclosure. Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, tablets, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown here, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0140] As shown in Figure 6 The electronic device 600 includes a computing unit 601 that can perform various appropriate actions and processes in accordance with a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0141] Various components in the electronic device 600 are connected to the I / O interface 605, including an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, a speaker, etc.; the storage unit 608, such as a magnetic disk, an optical disk, etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0142] The computing unit 601 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, and the like. The computing unit 601 performs various methods and processes described above, such as the article generation method. For example, in some embodiments, the article generation method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded onto the RAM 603 and executed by the computing unit 601, one or more steps of the article generation method described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the article generation method by any other appropriate means, such as by means of firmware.

[0143] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0144] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces a means for implementing the functions / acts specified in the flowcharts and / or block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, partially on a machine and partially on a remote machine or entirely on a remote machine or server.

[0145] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0146] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0147] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0148] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises from computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0149] Artificial intelligence is a discipline that studies enabling computers to simulate some thinking processes and intelligent behaviors of human beings (such as learning, reasoning, thinking, planning, etc.), both hardware and software technologies. Artificial intelligence hardware technology generally includes technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing, etc.; artificial intelligence software technology mainly includes computer vision technology, speech recognition technology, natural language processing technology, machine learning / deep learning technology, big data processing technology, knowledge graph technology, etc.

[0150] Cloud computing refers to accessing elastic and scalable shared physical or virtual resource pools through a network, and the resources can include servers, operating systems, networks, software, applications and storage devices, etc., and the resources can be deployed and managed in a self-service manner as needed. Through cloud computing technology, efficient and powerful data processing capabilities can be provided for artificial intelligence, blockchain and other technology applications and model training.

[0151] It should be understood that various forms of processes shown above can be used to reorder, add or delete steps. For example, each step described in the present disclosure can be executed in parallel, sequentially or in a different order, as long as the desired results of the technical solutions of the present disclosure can be achieved, which is not limited herein.

[0152] The above detailed description does not constitute a limitation on the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.

Claims

1. A method for generating an article, comprising: Obtaining a writing request, identifying the writing request, and obtaining writing requirements; Generating a draft outline and available materials based on the writing requirements; wherein generating available materials based on the writing requirements includes: Perform content retrieval according to the writing requirements to obtain target content; Generating editors with different perspectives based on the target content; The editor asks questions in a circular manner from his / her perspective to obtain writing questions; Split and rewrite the writing problem, and conduct content retrieval and summary to obtain usable materials; Rewriting the draft outline using the available materials to obtain a target article outline; Generate a target article according to the target article outline.

2. The method according to claim 1, wherein Generating a target article according to the target article outline includes: Extract the structure of the target article outline to obtain the outline hierarchical structure; An article is generated according to the outline hierarchical structure to obtain a target article.

3. The method according to claim 2, wherein: Generating an article according to the outline hierarchical structure to obtain a target article includes: According to the outline hierarchy, write chapters from top to bottom to generate the first draft of the article; The draft of the article is checked to obtain the target article.

4. The method according to claim 3, wherein: The first draft of the article is checked to obtain a target article, including: Conduct a combined check on the chapters in the first draft of the article to identify the erroneous and missing chapters; Rewrite the erroneous and missing chapters to obtain the target article.

5. The method according to claim 3, wherein The first draft of the article is checked to obtain a target article, including: According to the outline hierarchical structure, checking whether the chapters in the first draft of the article contain the designated materials; If it exists, the article draft is updated according to the specified material to obtain the target article.

6. The method according to claim 3, wherein: The first draft of the article is checked to obtain a target article, including: According to the outline hierarchical structure, the word count of the first draft of the article is checked from bottom to top to obtain a check result; The first draft of the article is updated according to the inspection result to obtain the target article.

7. The method according to claim 1, wherein After generating the target article according to the target article outline, the method further includes: Add footnotes and / or references to the target article.

8. The method according to claim 1, wherein After generating the target article according to the target article outline, the method further includes: Generate images and / or charts for the target article.

9. An article generating device, comprising: A writing requirement determination module is used to obtain a writing request, identify the writing request, and obtain writing requirements; The outline and material generation module is used to generate a draft outline and available materials according to the writing requirements. The available materials generated according to the writing requirements include: Perform content retrieval according to the writing requirements to obtain target content; Generating editors with different perspectives based on the target content; The editor asks questions in a circular manner from his / her perspective to obtain writing questions; Split and rewrite the writing problem, and conduct content retrieval and summary to obtain usable materials; A target outline determination module is used to rewrite the draft outline using the available materials to obtain a target article outline; The target article generating module is used to generate the target article according to the target article outline.

10. The device according to claim 9, wherein The target article generation module includes: A hierarchical structure extraction unit is used to extract the structure of the target article outline and obtain the outline hierarchical structure; The target article generating unit is used to generate an article according to the outline hierarchical structure to obtain a target article.

11. The device according to claim 10, wherein The target article generating unit includes: An article draft generation subunit is used to write chapters from top to bottom according to the outline hierarchical structure to generate an article draft; The target article determination subunit is used to check the first draft of the article to obtain the target article.

12. The device according to claim 11, wherein The target article determination subunit is specifically used for: Conduct a combined check on the chapters in the first draft of the article to identify the erroneous and missing chapters; Rewrite the erroneous and missing chapters to obtain the target article.

13. The device according to claim 11, wherein The target article determination subunit is specifically used for: According to the outline hierarchical structure, checking whether the chapters in the first draft of the article contain the designated materials; If it exists, the article draft is updated according to the specified material to obtain the target article.

14. The device according to claim 11, wherein The target article determination subunit is specifically used for: According to the outline hierarchical structure, the word count of the target article is checked from bottom to top to obtain a check result; The first draft of the article is updated according to the inspection result to obtain the target article.

15. The device according to claim 9, wherein The device further includes a footnote document adding module, which is used to: After generating the target article according to the target article outline, footnotes and / or references are added to the target article.

16. The device according to claim 9, wherein The apparatus further includes a graph generating module, configured to: After generating the target article according to the target article outline, pictures and / or charts are generated for the target article.

17. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the article generation method according to any one of claims 1 to 8.

18. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to enable a computer to execute the article generation method according to any one of claims 1 to 8.

19. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the article generation method according to any one of claims 1 to 8.

Images