Event description generation method, database generation method, device, and electronic device

Abstract

The present disclosure provides a method for generating event descriptions, which relates to the fields of artificial intelligence technology, and in particular to the fields of deep learning, natural language processing, large language models, and generative model technology. The specific implementation scheme is as follows: obtaining at least one of the title and abstract of the event to be processed as a retrieval text; using entity words extracted from the retrieval text, retrieving multiple relationships from a relational database, wherein the relationship includes a set of event elements, and the event element set includes time information, entity words, associated entity words, and entity relationship description texts for describing the relationship between entity words and associated entity words; and sorting multiple entity relationship description texts respectively from multiple relationships according to time information to obtain an event description of the event to be processed. The present disclosure also provides a database generation method, device, electronic device, and storage medium.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to the fields of deep learning, natural language processing, large language models, and generative model technology. More specifically, the present disclosure provides an event description generation method, a database generation method, an apparatus, an electronic device, a storage medium, and a computer program product. Background Art

[0002] Currently, news is usually displayed by sorting media content in a list format, requiring users to click on each one to understand the details of the news event. If users want to conduct in-depth research on key information in the news, they are required to search for relevant information again in the search box. This obviously cannot allow users to quickly understand the details of the news event and limits the depth of users' consumption of news content. Summary of the Invention

[0003] The present disclosure provides an event description generating method, a database generating method, an apparatus, an electronic device, a storage medium, and a computer program product.

[0004] According to a first aspect, a method for generating an event description is provided, which includes: obtaining at least one of a title and a summary of an event to be processed as a retrieval text; using entity words extracted from the retrieval text to retrieve multiple relationships from a relational database, wherein the relationship includes a set of event elements, and the event element set includes time information, entity words, associated entity words, and entity relationship description text for describing the relationship between the entity words and the associated entity words; and sorting multiple entity relationship description texts respectively from multiple relationships according to the time information to obtain an event description of the event to be processed.

[0005] According to the second aspect, a database generation method is provided, which includes: obtaining the news text and the time node of the news based on the news source web page information; extracting multiple entity words from the news text; generating entity relationship description text for describing the relationship between the multiple entity words; and generating a database based on the time node, entity words, and entity relationship description text.

[0006] According to a third aspect, an event description generating device is provided, which includes: a retrieval text determination module, used to obtain at least one of the title and summary of the event to be processed as a retrieval text; a first retrieval module, used to use entity words extracted from the retrieval text to retrieve multiple relationships from a relational database, wherein the relationship includes an event element set, and the event element set includes time information, entity words, associated entity words, and entity relationship description text for describing the relationship between the entity words and the associated entity words; and a first event description generating module, used to sort multiple entity relationship description texts respectively from multiple relationships according to time information to obtain an event description of the event to be processed.

[0007] According to the fourth aspect, a database generation device is provided, which includes: a parsing module for obtaining the news text and the time node of the news based on the news source web page information; an entity word determination module for extracting multiple entity words from the news text; an entity relationship description text generation module for generating an entity relationship description text for describing the relationship between multiple entity words; and a database generation module for generating a database based on the time node, entity words, and entity relationship description text.

[0008] According to a fifth aspect, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable 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 method provided according to the present disclosure.

[0009] According to a sixth aspect, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause a computer to execute the method provided according to the present disclosure.

[0010] According to a seventh aspect, a computer program product is provided, comprising a computer program stored on at least one of a readable storage medium and an electronic device, wherein the computer program implements the method provided according to the present disclosure when executed by a processor.

[0011] 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

[0012] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0013] Figure 1is a schematic diagram of an exemplary system architecture to which the event description generating method and the database generating method can be applied according to an embodiment of the present disclosure;

[0014] Figure 2 is a flowchart of a method for generating event descriptions according to one embodiment of the present disclosure;

[0015] Figure 3 is a system diagram of a method for generating event descriptions according to an embodiment of the present disclosure;

[0016] Figure 4 is a flowchart of a method for generating event descriptions according to another embodiment of the present disclosure;

[0017] Figure 5 is a flowchart of a database generation method according to one embodiment of the present disclosure;

[0018] Figure 6 is a flowchart of a database generation method according to another embodiment of the present disclosure;

[0019] Figure 7 is a block diagram of an event description generating apparatus according to an embodiment of the present disclosure;

[0020] Figure 8 is a block diagram of a database generating apparatus according to an embodiment of the present disclosure; and

[0021] Figure 9 The present invention is a block diagram of an electronic device according to at least one of an event description generating method and a database generating method in accordance with an embodiment of the present disclosure. DETAILED DESCRIPTION

[0022] 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.

[0023] Currently, news presentation primarily takes two forms. One is for users to click on a news item, redirecting them to a secondary page where various media outlets provide coverage of the news. A typical example is the trending news content on various search platforms. In this model, users click on the news list with great anticipation, only to be directed to a seemingly informative secondary page. However, this news browsing experience harbors significant inefficiencies. To delve deeper into the essence of each news item, users are forced to click through each page, navigating between pages. This process is not only tedious and time-consuming, but also exacerbates the inefficiency of information consumption, wasting users' precious time while failing to satisfy their desire for high-quality, in-depth news.

[0024] Another form of news presentation is video streaming, providing users with a seamless information acquisition experience. In this mode, when users finish watching a news video, the system automatically and smoothly jumps to the next video without manual operation, ensuring continuous playback of news information. This automated flow saves users time switching videos and manual operation. However, it also makes it easy to encounter duplicate video content, making it difficult to organize news nodes.

[0025] When users are immersed in consuming news content, both of the above solutions can be implemented, but neither can quickly sort out the ins and outs of the news, especially for news events with longer clues.

[0026] List-based presentations make it easy for users to encounter repeated exposure to news content as they scroll and click. Similar headlines and overlapping reporting angles cause already tight deadlines to slip by while browsing repetitive information, significantly reducing the efficiency and value of news acquisition. The quality and depth of news content are difficult to guarantee in this fragmented browsing experience. Each news item is compressed into a brief summary designed to attract clicks rather than present the full facts. This makes it difficult for users to obtain a comprehensive and in-depth understanding of the news, ultimately resulting in only superficial information fragments.

[0027] While video streaming has attracted a large audience with its intuitive, vivid, and instantaneous presentation, it also has significant limitations. One of the most significant issues is the repetitive nature of content and the fragmentation of context. In fast-moving video streams, to cater to viewers' fast-paced consumption habits, many news segments are cut short and concise, often focusing on a single moment or aspect of an event, lacking depth and breadth. This "fast-food" presentation method easily leads to the same or similar news content being replayed repeatedly at different times and on different platforms, causing viewers to experience information redundancy and fatigue. More importantly, the video streaming format struggles to effectively connect the complete storyline of a news event. Each video segment, while individually captivating, lacks a coherent narrative, making it difficult for viewers to grasp the cause, development, climax, and outcome of a news event as a whole. This fragmented information not only weakens the coherence and logic of the news but can also cause viewers to become lost in the vast amount of information, making it difficult to fully understand and comprehend the event. If you want to produce a sophisticated video stream that includes the context of events, you need to manually organize the news content first, which greatly increases the time cost of production.

[0028] It can be seen that although both solutions allow users to consume news content, neither can quickly and effectively obtain news information, especially news information containing long clues, which needs to be manually sorted and then output.

[0029] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0030] In the technical solution disclosed herein, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0031] Figure 1 This is a schematic diagram of an exemplary system architecture to which the event description generation method and database generation method can be applied according to an embodiment of the present disclosure. It should be noted that, Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0032] like Figure 1As shown, the system architecture 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0033] Users can use terminal devices 101, 102, 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 can be various electronic devices, including but not limited to smartphones, tablet computers, laptop computers, etc.

[0034] The event description generation method provided in the embodiment of the present disclosure can generally be executed by the terminal devices 101, 102, and 103. Accordingly, the database generation apparatus provided in the embodiment of the present disclosure can generally be set in the terminal devices 101, 102, and 103.

[0035] The database generation method provided in the embodiment of the present disclosure may generally be executed by the server 105. Accordingly, the database generation apparatus provided in the embodiment of the present disclosure may generally be provided in the server 105.

[0036] Figure 2 is a flowchart of a method for generating event description according to an embodiment of the present disclosure.

[0037] like Figure 2 As shown, the event description generating method 200 includes operations S210 to S230.

[0038] In operation S210 , at least one of a title and a summary of an event to be processed is obtained as a search text.

[0039] For example, in response to a user clicking on a news title in a news list or inputting information into a news search box, the title of the news event that the user wants to search for can be determined. The news event that the user wants to search for is the pending event.

[0040] The pending event may be a sequence of events with continuous progress. The news title clicked by the user may be the title of an event in the event sequence. The keywords in the user's input information may match the title of an event in the event sequence.

[0041] A news summary can be generated based on the news content under the title currently clicked by the user. For example, a large language model can be used to summarize the news content under the current title to obtain a news summary.

[0042] The news title and news summary can be combined to form a text as the search text. The search text is used to retrieve the event descriptions of multiple time nodes of the event to be processed, and the event descriptions of multiple time nodes constitute the event context.

[0043] In operation S220 , a plurality of relations are retrieved from a relational database using the entity words extracted from the search text.

[0044] For example, a trained deep learning model can be used to extract entity words from the retrieved text, which may include person entity words, location entity words, etc.

[0045] The trained deep learning model can be a fine-tuned large language model. For example, the large language model is fine-tuned using training sample text labeled with entity words, so that the large language model has the ability to recognize text entity words. The large language model can then be used to extract entity words from the search text.

[0046] A relational database can store multiple relationships, each of which can be a set of event elements. This set of event elements includes time information, entity words, associated entity words associated with the entity words, and entity relationship description text describing the relationship between the entity words and the associated entity words. This set of event elements can be extracted from a single news event, which can be an event in a sequence of events to be processed.

[0047] For example, the time information in the event element set can be the release time of the aforementioned single news event, which can be called a time node. The entity words and associated entity words can be related entity words extracted from the aforementioned single news event. The entity relationship description text can be the entity relationship description text between the entity words and associated entity words summarized using a trained deep learning model.

[0048] For example, a relationship stored in a relational database includes the entity word "Person A," the associated entity word "Person B," and the entity relationship description text "Person A bravely rescues Person B." Correspondingly, the relational database may also store another relationship, which includes the entity word "Person B," the associated entity word "Person A," and the entity relationship description text "Person A bravely rescues Person B." Therefore, a single event can also have multiple sets of event elements (i.e., relationships). The time nodes in multiple relationships extracted from a single event can be the same or consistent, where consistency can refer to the same time period, such as the same day.

[0049] In addition, as events progress, the relational database can also incrementally store the event element set of the latest progress event, which can include new time nodes, entity words, associated entity words and entity relationship description text extracted from the latest progress event.

[0050] Thus, for each entity word extracted from the search text, the relational database can be searched using that entity word. Each entity word can retrieve at least one relationship. For example, the same entity word can retrieve multiple relationships corresponding to multiple time nodes, while the entity relationship description text in the relationships at the same time node retrieved by different entity words may be the same.

[0051] In operation S230 , a plurality of entity relationship description texts respectively from a plurality of relationships are sorted according to time information to obtain an event description of the event to be processed.

[0052] For example, multiple entity relationship description texts in the retrieved multiple event element sets can be sorted according to time nodes, wherein the same entity relationship description texts at the same time node can be merged, and different entity relationship description texts at the same time node can be combined together.

[0053] The embodiments of the present disclosure extract entity words from news events, generate entity relationship description texts and store them in a database. When retrieving events, the entity relationship description texts are retrieved from the database and sorted according to time nodes to obtain event descriptions containing time clues. This can effectively sort out the context of events and improve users' comprehensive knowledge and deep understanding of news events.

[0054] Figure 3 4 is a system diagram of a method for generating event descriptions according to an embodiment of the present disclosure.

[0055] like Figure 3As shown, this embodiment includes an entity extraction module 310, a relational database 320 and a feature database 330. The entity extraction module 310 is used to perform entity extraction on at least one of the title and summary of the event to be processed, and obtain entity words and entity description texts. The relational database 320 stores multiple relationships, each of which can be a set of event elements extracted from a single news event, including a time node, an entity word, an associated entity word associated with the entity word, and an entity relationship description text for describing the relationship between the entity word and the associated entity word. The feature database 330 stores the feature vectors and time nodes of the above-mentioned entity relationship description texts. That is to say, after extracting the event element set from a single news event, in addition to storing the event element composition relationship in the event element set in the relational database 320, the entity relationship description text in the event element set is also encoded to obtain entity relationship description features, and the entity relationship description features and the time nodes are stored in the feature database 330.

[0056] According to an embodiment of the present disclosure, in response to at least one of a click operation on a news title and an input operation in a news search box, the title of the event to be processed is determined; and a summary is generated based on the news content under the title.

[0057] For example, a user can click on a title on a news list or enter information in a news search box to determine the desired news title. The content under the title can be summarized to generate a summary. The entity extraction module 310 can be a fine-tuned large language model that extracts entity words from the search text and further generates descriptive text for the entity words, i.e., entity description text.

[0058] For example, the entity extraction module 310 extracts the entity word "Character A" and generates an entity description text "Progress of the incident in which Character A bravely performs a righteous act".

[0059] Next, the relational database 320 can be searched using the entity terms. This search process can be an exact match process. That is, if the entity terms completely match the content of the entity term field in the relational database, at least one relationship of the entity term can be retrieved. Then, by sorting the entity relationship description text in at least one relationship in chronological order, an event description containing time clues, i.e., an event context, can be obtained.

[0060] According to an embodiment of the present disclosure, entity description text of an entity word is generated; multiple entity relationship description features are retrieved from a feature database using the entity description text, wherein the feature database includes the entity relationship description features and time information of the entity relationship description text; and multiple entity relationship description texts corresponding to each of the multiple entity relationship description features are sorted according to the time information to obtain an event description of the event to be processed.

[0061] For example, the entity relationship description text can be used to search the feature database 330, and the search process can be a fuzzy matching process. For example, a feature vector of the entity description text, i.e., the entity description feature, can be generated, the similarity between the entity description feature and the entity relationship description features in the feature database 330 can be calculated, and multiple entity relationship description features with the highest similarity can be selected. Multiple entity relationship description texts corresponding to the selected multiple entity relationship description features are determined, and the multiple entity relationship description texts are sorted in chronological order to obtain an event description containing time clues, i.e., an event context.

[0062] The embodiments of the present disclosure provide two matching methods for retrieval: exact matching and fuzzy matching, which can improve the comprehensiveness of the retrieval of the events to be processed.

[0063] In one example, when multiple relations are retrieved from the relational database 320 , since the retrieval of the relational database 320 is an exact match, the multiple relations retrieved can accurately describe the ins and outs of the event, and therefore, fuzzy matching of the feature database 330 is no longer required.

[0064] Figure 4 is a flowchart of a method for generating event description according to another embodiment of the present disclosure.

[0065] like Figure 4 As shown, this embodiment includes operations S401 to S410.

[0066] In operation S401 , at least one of a title and a summary of an event to be processed is obtained as a search text.

[0067] In operation S402, entity words are extracted from the search text.

[0068] In operation S403, the relational database is searched using the entity words.

[0069] The implementation steps of operations S401 to S403 are similar to those of operations S210 to S220 and are not described in detail here. It should be noted that the step of searching the relational database using entity words in operation S403 is an exact match.

[0070] In operation S404, it is determined whether a relationship that meets the preset conditions is retrieved from the relationship database. If yes, operation S405 is executed; otherwise, operation S406 is executed.

[0071] According to an embodiment of the present disclosure, entity words are used to retrieve multiple relationships that meet preset conditions from a relational database, wherein the preset conditions include at least one of a time condition and a source condition.

[0072] For example, each relationship in a relational database can be a set of event elements extracted from a single news event. This set of event elements can also include the source information of the news event. For example, the source information of the news event can be determined based on the source URL (Uniform Resource Locator) of the single news event, and this source information can also be added to the relationship.

[0073] When using entity words to search a relational database, you can also set search conditions. The search conditions can limit the time range and source range. For example, the time range can be within the past year, and the source range can be several designated authoritative news websites.

[0074] If multiple relationships meeting the above search conditions are retrieved, operation S405 may be executed; otherwise, operation S406 may be executed.

[0075] In operation S405 , the entity relationship description texts in the retrieved multiple relationships are sorted according to time information to obtain an event context.

[0076] For example, the event element set includes time information. By sorting the multiple entity relationship description texts in the multiple relationships retrieved according to the time information in the multiple relationships, an event description containing time clues, namely, an event context, can be obtained.

[0077] In operation S406 , entity description text is generated.

[0078] The entity description text may be a description text of the extracted entity word. For example, if the entity word is "Character A", the entity description text may be "Progress of the incident in which Character A bravely performs a righteous act".

[0079] In operation S407, the entity description text is used to search the feature database.

[0080] For example, the entity description text is encoded to obtain entity description features, and the similarity between the entity description features and the entity relationship description features in the feature database is calculated. This process is a fuzzy matching process.

[0081] In operation S408, it is determined whether entity relationship description features that meet the preset conditions are retrieved from the feature database. If so, operation S409 is executed. Otherwise, it means that there are no retrieval results and the process ends.

[0082] For example, the source information of the event (such as the source webpage URL) may also be added to the feature database, that is, the feature database includes the entity relationship description feature and the time information and source information corresponding to the entity relationship description feature.

[0083] According to an embodiment of the present disclosure, entity description features are used to retrieve multiple entity relationship description features that meet preset conditions from a feature database, wherein the preset conditions include at least one of a time condition and a source condition.

[0084] For example, search conditions can also be set for fuzzy matching of the feature database. The search conditions can limit the time range and source range. For example, the time range can be within the past year, and the source range can be several designated authoritative news websites.

[0085] If multiple entity relationship description features that meet the search conditions are retrieved, operation S409 may be performed.

[0086] In operation S409 , the multiple entity relationship description texts corresponding to the retrieved multiple entity relationship description features are sorted according to time information to obtain an event context.

[0087] For example, each entity relationship description feature corresponds to time information. Multiple entity relationship description texts corresponding to multiple entity relationship description features can be sorted according to the time information to obtain an event description containing time clues, that is, an event context.

[0088] In operation S410 , an event context is displayed.

[0089] For example, by displaying multiple entity relationship description texts in the order in which they are arranged, users can quickly obtain the ins and outs of a news event and conduct in-depth consumption of the news event.

[0090] According to an embodiment of the present disclosure, the present disclosure also provides a database generation method.

[0091] Figure 5 is a flowchart of a database generation method according to an embodiment of the present disclosure.

[0092] like Figure 5 As shown, the database generation method 500 includes operations S510 to S540.

[0093] In operation S510, the news text and the time node of the news are obtained according to the news source web page information.

[0094] For example, for news titles on the hot search list, we can obtain the news source webpage information, and then extract the text and release time from the news source webpage. The release time can be used as the time node of the news.

[0095] In operation S520 , a plurality of entity words are extracted from the news text.

[0096] In operation S530 , entity relationship description text for describing the relationship between the plurality of entity words is generated.

[0097] For example, a fine-tuned large language model can be used to extract entities from a news article, obtaining multiple entity words. The large language model can also generate entity relationship description text that describes the relationships between multiple entity words.

[0098] For example, the large language model can be prompted to summarize the news text in the same way as that used to describe entity relationships, thereby obtaining entity relationship description text.

[0099] For example, the large language model extracts the entity words "person A", "person B", and "address C" from the news text and generates the entity relationship description text "person A is a delivery man at location C, and person A bravely rescues person B."

[0100] In operation S540 , a database is generated according to the time nodes, entity words, and entity relationship description texts.

[0101] For example, a time node, entity word, entity relationship description text, and associated entity word can be combined into a relationship and stored in a relational database. Alternatively, the entity relationship description text can be encoded to obtain an entity relationship description vector and stored in a feature database.

[0102] The embodiments of the present disclosure extract time nodes and entity words from the news text to generate entity relationship description text for describing entity relationships, and store the time nodes, entity words, and entity relationship description text in a database, which can be used for event retrieval, so as to retrieve the event context containing time clues, enable users to quickly obtain the ins and outs of news events, and improve users' comprehensive knowledge and deep understanding of news events.

[0103] Figure 6 is a flowchart of a database generation method according to another embodiment of the present disclosure.

[0104] like Figure 6 As shown, this embodiment includes operations S610 to S670.

[0105] In operation S610, news source web page information is acquired according to the news title.

[0106] In operation S620, the news text, time node, and source information are determined from the news source web page information.

[0107] According to an embodiment of the present disclosure, source information is determined based on news source web page information, news content at multiple time nodes in the news source web page information with intervals not exceeding a preset time length is determined as news text; and the time node of the news is determined from the multiple time nodes.

[0108] For example, the source information can be the URL of a source webpage. The time point can be the time of news release. There can be one or more source webpages obtained based on the news headline. If there are multiple source webpages, the content of the multiple original webpages can be merged together. Within the merged source webpage content, news content from multiple time points with a time interval not exceeding a preset length (e.g., 24 hours) can be combined to form the main body of the news. The earliest of the multiple time points can be used as the time point of the news.

[0109] In operation S630, the news text is segmented to obtain text block contents.

[0110] In operation S640, entity words are extracted from each text block, and the extracted multiple text blocks are summarized to obtain text relationship description text.

[0111] The purpose of segmenting the news article is to ensure that the resulting text blocks meet the model's requirements. For example, a news article can be segmented into blocks of 2,000 words. These blocks are then fed into a fine-tuned large language model, which extracts multiple entity words.

[0112] The large language model can also be prompted to summarize the news text in a summary manner that describes entity relationships to obtain entity relationship description text.

[0113] In operation S650 , the time node, the entity word, the associated entity word, the entity relationship description text, and the source information are combined into a relationship and stored in a relational database.

[0114] For example, the time node is "X year X month X day", the source information is the original web page URL identifier, the extracted entity words include "person A", "person B", and "address C", and the generated entity relationship description text is "person A is a delivery man in place C, and person A bravely rescues person B."

[0115] For the entity word "Person A", a relationship can be formed: {"time": "X year X month X day", "entity word": "Person A", associated entity words: "Person B", "Address C", "entity relationship description text": "Person A is a delivery driver at location C, and Person A bravely rescues Person B"}.

[0116] For the entity word "Person B", a relationship can be formed: {"time": "X year X month X day", "entity word": "Person B", associated entity words: "Person A", "Address C", "Entity relationship description text": "Person A is a delivery driver at location C, and Person A bravely rescues Person B"}.

[0117] For the entity word "address C", a relationship can be formed: {"time": "X year X month X day", "entity word": "address C", associated entity words: "person A", "person B", "entity relationship description text": "person A is a delivery man at location C, and person A bravely rescues person B"}.

[0118] Therefore, the entity relationship description texts in the relationships of different entity words at the same time node can be the same.

[0119] Furthermore, as events progress, relational databases can incrementally store new relationships. For example, a set of event features extracted from the latest news about an event can be combined with new time points, entity words, associated entity words, and entity relationship description text to form a new relationship. Therefore, the same entity word can be associated with multiple relationships at different time points.

[0120] In operation S660 , a feature vector of the entity relationship description text is generated to obtain entity relationship description features.

[0121] In operation S670 , the entity relationship description features, time nodes, and source information are stored in a feature database.

[0122] In another example, the entity relationship description text may be encoded to obtain entity relationship description features, and the entity relationship description features, time nodes, and source information may be stored in a feature database.

[0123] The above-mentioned relational database can use entity words for precise matching in the retrieval stage to obtain entity relationship description texts at multiple time points, and then sort them according to time nodes to obtain the event context.

[0124] The above-mentioned feature database can use entity description text for fuzzy matching in the retrieval stage to obtain multiple entity relationship description features, and sort the multiple entity relationship description texts corresponding to the multiple entity relationship description features according to time nodes to obtain the event context.

[0125] The embodiments of the present disclosure, by constructing a relational database and a feature database, can perform precise matching and fuzzy matching in the retrieval stage, thereby improving the comprehensiveness of the retrieval of events to be processed.

[0126] According to an embodiment of the present disclosure, the present disclosure also provides an event description generating device and a database generating device.

[0127] Figure 7 It is a block diagram of an event description generating apparatus according to an embodiment of the present disclosure.

[0128] like Figure 7As shown, the event description generating device 700 includes a search text determining module 710 , a first search module 720 and a first event description generating module 730 .

[0129] The search text determination module 710 is configured to obtain at least one of a title and a summary of the event to be processed as a search text.

[0130] The first retrieval module 720 is used to use entity words extracted from the retrieval text to retrieve multiple relationships from the relational database, where the relationship includes a set of event elements, the event element set includes time information, entity words, associated entity words, and entity relationship description text used to describe the relationship between the entity words and the associated entity words.

[0131] The first event description generating module 730 is used to sort the entity relationship description texts from the multiple relationships according to time information to obtain the event description of the event to be processed.

[0132] The event description generating device 700 further includes an entity description text generating module, a second retrieval module and a second event description generating module.

[0133] The entity description text generation module is used to generate entity description text for entity words.

[0134] The second retrieval module is used to retrieve a plurality of entity relationship description features from a feature database using the entity description text, wherein the feature database includes the entity relationship description features of the entity relationship description text and time information.

[0135] The second event description generating module is used to sort the multiple entity relationship description texts corresponding to the multiple entity relationship description features according to time information to obtain the event description of the event to be processed.

[0136] According to an embodiment of the present disclosure, the feature database further includes source information.The second retrieval module includes an entity description feature generation unit and a retrieval unit.

[0137] The entity description feature generation unit is used to generate entity description features of the entity description text.

[0138] The retrieval unit is used to retrieve a plurality of entity relationship description features that meet preset conditions from a feature database using the entity description features, wherein the preset conditions include at least one of a time condition and a source condition.

[0139] The search text determination module 710 includes a title determination unit and an abstract generation unit.

[0140] The title determination unit is configured to determine a title of the event to be processed in response to at least one of a click operation on a news title and an input operation in a news search box.

[0141] The summary generation unit is used to generate a summary according to the news content under the title.

[0142] The first retrieval module 720 is further configured to use entity words to retrieve a plurality of relationships that meet preset conditions from the relational database, wherein the preset conditions include at least one of a time condition and a source condition.

[0143] The event description generating device 700 further includes a display module.

[0144] The display module is used to display event descriptions in the order of multiple entity relationship description texts.

[0145] Figure 8 is a block diagram of a database generating apparatus according to an embodiment of the present disclosure.

[0146] like Figure 8 As shown, the database generating device 800 includes a parsing module 810 , an entity word determining module 820 , an entity relationship description text generating module 830 and a database generating module 840 .

[0147] The parsing module 810 is used to obtain the news text and the time node of the news based on the news source web page information.

[0148] The entity word determination module 820 is used to extract multiple entity words from the news text.

[0149] The entity relationship description text generation module 830 is used to generate entity relationship description text for describing the relationship between multiple entity words.

[0150] The database generation module 840 is used to generate a database based on time nodes, entity words, and entity relationship description texts.

[0151] The database generation module 840 includes an associated entity word determination unit, an entity relationship description subtext determination unit, and a first storage unit.

[0152] The associated entity word determining unit is used to determine, for each entity word, an associated entity word having an associated relationship with the entity word from a plurality of entity words.

[0153] The entity relationship description subtext determining unit is used to determine the entity relationship description subtext used to describe the relationship between the entity word and the associated entity word from the entity relationship description text.

[0154] The first storage unit is used to store a relationship composed of a time node, an entity word, an associated entity word, and an entity relationship description subtext in a relational database.

[0155] The database generation module 840 further includes an entity relationship description feature generation unit and a second storage unit.

[0156] The entity relationship description feature generation unit is used to generate entity relationship description features of the entity relationship description text.

[0157] The second storage unit is used to store the time nodes and entity relationship description features in the feature database.

[0158] The database generating device 800 further includes a source information determining module and an adding module.

[0159] The source information determination module is used to determine the source information based on the news source web page information.

[0160] The adding module is used to add source information to the relational database and the feature database.

[0161] The parsing module 810 includes a source web page acquisition unit, a text determination unit, and a time node determination unit.

[0162] The source web page acquisition unit is used to acquire news source web page information according to news titles.

[0163] The main text determination unit is used to determine the news contents of multiple time nodes in the news source web page information with an interval not exceeding a preset time length as the news main text.

[0164] The time node determination unit is used to determine the time node of the news from multiple time nodes.

[0165] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0166] Figure 9 A schematic block diagram of an example electronic device 900 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 laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0167] like Figure 9As shown, device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. RAM 903 may also store various programs and data required for the operation of device 900. Computing unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 905 is also connected to bus 904.

[0168] Various components in the device 900 are connected to the I / O interface 905, including an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a magnetic disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0169] The computing unit 901 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 901 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 suitable processor, controller, microcontroller, etc. The computing unit 901 performs the various methods and processes described above, such as at least one of the event description generation method and the database generation method. For example, in some embodiments, at least one of the event description generation method and the database generation method can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of at least one of the event description generation method and the database generation method described above can be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to execute at least one of the event description generating method and the database generating method in any other appropriate manner (eg, by means of firmware).

[0170] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-a-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0171] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0172] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, 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), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0173] 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).

[0174] 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 with 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.

[0175] Computer systems may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The client and server relationship arises through computer programs running on the respective computers and having a client-server relationship to each other.

[0176] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0177] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A method for generating an event description, comprising: Obtain at least one of a title and a summary of an event to be processed as a search text; Searching a relational database using entity words extracted from the search text; In response to retrieving a plurality of relations that meet preset conditions from the relational database, sorting a plurality of entity relationship description texts respectively from the plurality of relations according to time information to obtain an event description of the event to be processed, wherein the relations include an event element set, the event element set including time information, entity words, associated entity words, and entity relationship description texts used to describe the relationship between the entity words and the associated entity words; In response to no relationship meeting the preset condition being retrieved from the relational database, generating an entity description text of the entity word; Using the entity description text to search a feature database, wherein the feature database includes entity relationship description features of the entity relationship description text and the time information; In response to retrieving multiple entity relationship description features that meet the preset conditions from the feature database, multiple entity relationship description texts corresponding to the multiple entity relationship description features are sorted according to the time information to obtain an event description of the event to be processed.

2. The method according to claim 1, wherein The feature database also includes source information; and searching the feature database using the entity description text includes: Generating entity description features of the entity description text; The entity description feature is used to retrieve a plurality of entity relationship description features that meet preset conditions from the feature database, wherein the preset conditions include at least one of a time condition and a source condition.

3. The method according to claim 1, wherein The obtaining of at least one of the title and summary of the event to be processed as the search text includes: In response to at least one of a click operation on a news title and an input operation in a news search box, determining a title of the event to be processed; The summary is generated according to the news content under the title.

4. The method according to claim 1, wherein The event element set also includes source information; the preset condition includes at least one of a time condition and a source condition.

5. The method according to any one of claims 1 to 4, further comprising: The event description is displayed according to the arrangement order of the multiple entity relationship description texts.

6. A method for generating a database, comprising: According to the news source webpage information, obtain the news text and news time node; Extracting multiple entity words from the news text; Generating entity relationship description text for describing the relationship between the plurality of entity words; as well as Generate a database according to the time node, the entity word, and the entity relationship description text; The database is used to implement the method of claim 1.

7. The method according to claim 6, wherein: The database includes a relational database; generating the database according to the time node, the entity word, and the entity relationship description text includes: for each entity word, Determining, from the plurality of entity words, an associated entity word having an associated relationship with the entity word; Determining an entity relationship description subtext for describing the relationship between the entity word and the associated entity word from the entity relationship description text; and The time node, the entity word, the associated entity word, and the entity relationship description subtext are combined into a relationship and stored in the relationship database.

8. The method according to claim 7, wherein: The database also includes a feature database; Generating a database according to the time node, the entity word, and the entity relationship description text further includes: Generating entity relationship description features of the entity relationship description text; as well as The time node and the entity relationship description features are stored in the feature database.

9. The method according to claim 8, further comprising: Determining source information based on the news source webpage information; as well as The source information is added to the relational database and the feature database.

10. The method according to claim 6, wherein: The method of obtaining the news text and the time point of the news based on the news title and news source webpage information includes: Get news source web page information based on news titles; Determining the news content of multiple time nodes in the news source webpage information at intervals not exceeding a preset time as the news body; and A time node of the news is determined from the multiple time nodes.

11. An event description generating device, comprising: A search text determination module, configured to obtain at least one of a title and a summary of an event to be processed as a search text; A first retrieval module is used to search a relational database using entity words extracted from the retrieval text; a first event description generating module configured to, in response to retrieving a plurality of relations meeting preset conditions from the relational database, sort a plurality of entity relationship description texts respectively from the plurality of relations according to time information to obtain an event description of the event to be processed, wherein the relations include an event element set, the event element set including time information, entity words, associated entity words, and entity relationship description texts for describing the relationship between the entity words and the associated entity words; an entity description text generating module, configured to generate an entity description text of the entity word in response to no relationship meeting a preset condition being retrieved from the relational database; a second retrieval module, configured to use the entity description text to search a feature database, wherein the feature database includes entity relationship description features of the entity relationship description text and the time information; The second event description generation module is used to respond to retrieving multiple entity relationship description features that meet the preset conditions from the feature database, sort the multiple entity relationship description texts corresponding to the multiple entity relationship description features according to the time information, and obtain the event description of the event to be processed.

12. The device according to claim 11, wherein The feature database also includes source information; the second retrieval module includes: An entity description feature generating unit, configured to generate entity description features of the entity description text; The retrieval unit is configured to retrieve a plurality of entity relationship description features that meet preset conditions from the feature database using the entity description features, wherein the preset conditions include at least one of a time condition and a source condition.

13. The device according to claim 11, wherein The retrieval text determination module includes: a title determination unit, configured to determine the title of the event to be processed in response to at least one of a click operation on a news title and an input operation in a news search box; The summary generating unit is used to generate the summary according to the news content under the title.

14. The device according to claim 11, wherein The event element set also includes source information; the preset condition includes at least one of a time condition and a source condition.

15. The apparatus according to any one of claims 11 to 14, further comprising: The display module is used to display the event description according to the arrangement order of the multiple entity relationship description texts.

16. A database generating device, comprising: The parsing module is used to obtain the news text and the time node of the news based on the news source web page information; An entity word determination module, configured to extract a plurality of entity words from the news text; An entity relationship description text generation module, used to generate entity relationship description text for describing the relationship between the multiple entity words; as well as A database generation module, configured to generate a database based on the time node, the entity word, and the entity relationship description text; The database is used to implement the device of claim 11.

17. The device according to claim 16, wherein The database includes a relational database; the database generation module includes: an associated entity word determining unit, configured to determine, for each entity word, an associated entity word having an associated relationship with the entity word from the plurality of entity words; An entity relationship description subtext determining unit, configured to determine, from the entity relationship description text, an entity relationship description subtext for describing the relationship between the entity word and the associated entity word; The first storage unit is configured to store a relationship composed of the time node, the entity word, the associated entity word, and the entity relationship description subtext in the relational database.

18. The device according to claim 17, wherein The database also includes a feature database; The database generation module also includes: An entity relationship description feature generating unit, configured to generate entity relationship description features of the entity relationship description text; as well as The second storage unit is used to store the time node and the entity relationship description feature in the feature database.

19. The apparatus according to claim 18, further comprising: A source information determination module, configured to determine source information based on the news source webpage information; as well as An adding module is used to add the source information to the relational database and the feature database.

20. The apparatus according to claim 16, wherein The parsing module includes: A source webpage acquisition unit, configured to acquire news source webpage information based on news titles; a text determination unit, configured to determine news contents at multiple time points in the news source webpage information with intervals not exceeding a preset time length as the news text; and A time node determination unit is used to determine the time node of the news from the multiple time nodes.

21. 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 perform the method according to any one of claims 1 to 10.

22. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 10.

23. A computer program product, comprising a computer program, wherein the computer program is stored on at least one of a readable storage medium and an electronic device, and when the computer program is executed by a processor, implements the method according to any one of claims 1 to 10.

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