9 results about "Automatic summarization" patented technology

An information handling system includes a data store configured to store an account associated with a first alert and a second alert. A processor may receive the first alert and the second alert, map the first alert and the second alert to a first node and a second node of a causality graph, traverse the causality graph starting from the sink node to the source node to determine an association between the first alert and the second alert, and generate the account based on the association between the first alert and the second alert.

The invention relates to the technical field of asset valuation, and discloses a material disposal condition automatic summarization and asset valuation verification method, which comprises the following steps of: obtaining attribute feature vectors of new warehousing materials, screening a reference category set from a mature material category library, and fusing a multiple regression model parameter set of a reference category to generate prior parameter distribution; limited historical records of new warehousing materials are extracted to generate a multivariate sample data set, posterior regression parameter distribution is generated based on Bayesian fusion, a prediction residual value rate and prediction uncertainty are calculated, a prediction estimated value is generated and compared with a declaration estimated value, a verification result is output, and the problem that multi-factor residual value prediction is insufficient in accuracy under the small sample condition is solved.

A computer-implemented method and computer system improvements for automatically generating a summary note related to a digitally-recorded interlocutor conversation, such as a chat transcript, including accessing a data corpus having at least one digitally-recorded conversation of text-based interlocutory conversation(s) including at least one labeled value, extracting at least one Conversation Feature from the labeled value(s), creating at least one Summary Feature from the extracted Conversation Feature(s), generating at least one Summary Note by combining one or more Narrative Structures with the Summary Feature(s), and digitally outputting the at least one Summary Note.

The invention discloses an archive text automatic abstracting and knowledge question-answering system combined with a large model, and relates to the technical field of text automatic abstracting and question-answering. Comprising a text scanning module for performing text scanning on a target archive text, and extracting text structured data through an entity recognition model to obtain text information items; and the information screening module is used for carrying out information screening on the basis of the text information items, screening the information into judgment data and non-judgment data, taking the judgment information as input data, and generating matching information corresponding to the input data through a large language model. According to the method, the file noise interference is resisted through a triple mechanism, the cross-modal alignment technology performs joint analysis on the OCR text, the seal image and the table data, so that the fuzzy character recognition accuracy is improved, the mapping relation between the key information item and the alignment information item is established, and the spelling dislocation problem of the digital file is solved.

The invention provides a simple daily report filling and data analysis method and system, and relates to the technical field of data management.The method comprises the steps that daily report data submitted by a user through a front-end customized filling interface is received, and the daily report data is structured daily report data formed based on a preset department business field template; associating the structured daily report data with the user and department information through an object relation mapping framework, and storing the structured daily report data and the user and department information in a preset database to form an associatively stored daily report data set; based on the associatively stored daily report data set, performing automatic summarization analysis through a preset multi-dimensional statistical rule, and generating a quantitative statistical result; and the quantitative statistical result is converted into a visual chart to be displayed so as to support management decision making. According to the method, the problems of difficulty in daily report data aggregation calculation, visualization deficiency and insufficient decision support in the prior art can be solved.

The present invention relates to an intelligent surveillance and automatic summarization method for generating a summary video by automatically determining a summarization segment of an original image. The intelligent surveillance and automatic summarization method comprises the steps of: (a) receiving an original image to detect objects and storing information about the detected objects; (b) calculating a congestion level of the objects in the original image to determine a segment for summary video generation in the original image; and (c) generating and storing a summary video according to the determined segment.

The application is an automatic summarization fact enhancement method based on syntax structure reduction, and relates to big data resource service and data mining technology.The method comprises the following steps: analyzing the syntax structure of a to-be-corrected abstract text to obtain a corresponding syntax dependency tree; performing postorder traversal on the syntax dependency tree, attempting to prune a sub-tree according to a syntax dependency relationship, obtaining a candidate abstract, and restoring the pruned sub-tree before sequentially accessing a next sub-tree; pre-deploying multiple fact evaluation index models on a server; calling the multiple fact evaluation index models on the server in real time to evaluate the fact of each candidate abstract; when a next abstract is better than a previous abstract on all fact evaluation indexes, the next abstract is considered to be better, and the best abstract is output.The application first applies syntax structure reduction to a post-correction scheme, greatly expands the range of errors that can be coped with, improves the fact accuracy of an automatic summarization model in generating an abstract, and can be widely used in existing automatic summarization models.

This invention discloses a financial statement summary generation system based on artificial intelligence-generated content, belonging to the field of artificial intelligence technology. The system performs segmentation and extraction of cited fragments from the financial statement text, constructs a closed-loop structure by combining candidate fragments from footnotes, and classifies the closure status through correspondence comparison. It then performs backfilling expansion and constraint reconstruction on the closed structure to generate summary generation primitives containing the main body of the matter, the main body of the explanation, and the main body of the limitation. Finally, it verifies the structural consistency and semantic integrity of the summary generation primitives through checks on the clarity of the matter, the correspondence of the explanation, and the completeness of the limitation. This system, focusing on the construction and verification process of the closed-loop structure, structurally reconstructs the cross-segment relationships between the main body of the financial statement and the footnotes, ensuring that the summary content has a clear subject focus, consistent sources of explanation, and complete limiting conditions. It is suitable for scenarios involving automatic financial statement summary generation and assisted review.

The application discloses a cross-file information summarization and new knowledge automatic summarization method and system, and belongs to the technical field of file data processing. The method specifically comprises the following steps: collecting document file data, performing vectorization on the document file data based on a semantic embedding model, reconstructing and performing semantic clustering on the semantic vectors of the document file data, constructing a semantic structure graph, wherein the semantic structure graph represents the logical relationship between the document file data, performing content completion on the nodes with incomplete association in the semantic structure graph, extracting structured knowledge units from the completed semantic structure graph, and generating a new knowledge text based on the structured knowledge units. The application solves the problems of knowledge fragmentation between traditional document files and low efficiency of manual summarization, significantly improves the automation degree of knowledge discovery, and is suitable for scenarios such as knowledge base construction, field rule extraction, field knowledge discovery and the like.