41 results about "Multi-document summarization" patented technology

A system for generating a summary of a plurality of documents and presenting the summary information to a user is provided which includes a computer readable document collection containing a plurality of related documents stored in electronic form. Documents can be pre-processed to group documents into document clusters. The document clusters can also be assigned to predetermined document categories for presentation to a user. A number of multiple document summarization engines are provided which generate summaries for specific classes of multiple documents clusters. A summarizer router is employed to determining a relationship of the documents in a cluster and select one of the document summarization engines for use in generating a summary of the cluster. A single event engine is provided to generate summaries of documents which are closely related temporally and to a specific event. A dissimilarity engine for multiple document summary generation is provided which generates summaries of document clusters having documents with varying degrees of relatedness. A user interface is provided to display categories, cluster titles, summaries, related images.

The invention relates to a method which extracts the abstracts and key words of a file at the same time, belonging to language words processing technique. The existing method takes the extraction of abstracts of the file and the extraction of the key words of the file as two irrelative tasks and respectively processes the two tasks which have the same nature; the method can utilize the same nature of the extraction and completes the extractions of the abstracts and key words at the same time. The method utilizes a figure learning model and comprehensively utilizes the relationships between sentences in the file, between the sentence and the words in the file, and between the words in the file, exactly evaluates the importance of the sentences and the words, and finally adopts the important sentences and words as the abstracts and key words of the file. The method can extract the abstracts and key words of the file at the same time on the one hand, and can gain a better effect of the extraction of abstracts and key words on the other hand; the method can be widely applied to the fields such as text information processing and digging and the like.

The invention belongs to the technical field of multi-document summarization and provides a multi-document summarization method based on text segmentation, which comprises the following steps of: using HowNet to obtain a concept, building a concept vector space model, conducting text segmentation by adopting an improved DotPlotting model and a sentence concept vector space, calculating sentence weight by using the built concept vector space model, generating a summary according to the sentence weight, the text segmentation and the similarity situation, and evaluating the generated summary by using the ROUGE-N evaluation method and using F_Score as an evaluation index. According to the result, the multi-document summarization by using a text segmentation technique is effective, relevant documents provided by users can be gathered to form a summary by adopting the multi-document summarization method, the summary is displayed to the users in a proper way, the information acquisition efficiency is greatly improved, the practicability is high and the popularization and application values are greater.

The invention discloses an automatic multi-document abstract extraction method and an automatic multi-document abstract extraction system based on sentence vectors. The automatic multi-document abstract extraction method includes S1, preprocessing document collections; S2, generating the sentence vectors through doc2vec model training; S3, cluttering the sentence vectors into sub-theme documents;S4, creating a sentence relation graph model in each sub-theme document; S5, calculating sentence weights; S6, extracting and sequencing sentences to form abstracts. The automatic multi-document abstract extraction method and the automatic multi-document abstract extraction system have the advantages that all the sentences in the target document collections are expressed by the vectors through thelarge-corpus-set training doc2vec model; sub themes are acquired through spectral clustering, one sentence is extracted from each sub theme, and accordingly, sentence redundancy is avoided; the sentences are sequenced according to positions in original documents to form the abstracts, and coherence of the abstract sentences is improved.

To overcome the defect in prior art, the invention calculated the true semantic relation with diffusion character of sentence relation, and makes a difference between the sentences inside the document and within documents. This invention has well effect in practical evaluating.

Multimedia document summarization techniques are described. That is, given a document that includes text and a set of images, various implementations generate a summary by extracting relevant text segments in the document and relevant segments of images with constraints on the amount of text and number/size of images in the summary.

The invention relates to multiple-document summarization using document clustering. Systems and methods are disclosed for summarizing multiple documents by generating a model of the documents as a mixture of document clusters, each document in turn having a mixture of sentences, wherein the model simultaneously representing summarization information and document cluster structure; and determininga loss function for evaluating the model and optimizing the model.

A method for searching multiple documents on a computer system includes steps for sending a query to a system core where the query is passed to a search component for searching the documents. The system core in turn receives results from the search component indicating related documents to the query and passes to a summarization component a specified number of the results. The summarization component processes related documents corresponding to the specified number of results and removes duplicate results to produce a multi-document summary. The system core receives the summary from the summarization component. The multi-document summary is received from the system core.

The invention provides a multi-document abstract generation method which comprises the following steps: S1, determining a theme, acquiring a plurality of documents related to the theme, and constructing a first corpus; S2, constructing an HLDA topic model for the topic, and obtaining sub-topics; S3, calculating importance scores of the clauses; S4, calculating the importance degree of the sub-topics; and S5, extracting abstract sentences. According to the method, news features are added, an HLDA theme importance calculation method is improved, reasonable sentence scores are obtained, and meanwhile on the basis of a traditional abstract sorting step, features of inter-sentence information are added to serve as one of bases for judging sentence sorting, so that finally obtained abstract sentences are more accurate, and sentences are smoother.

The invention relates to an unsupervised scientific and technological intelligence abstract automatic generation method based on multi-sentence compression, and belongs to the technical field of natural language generation. Aiming at multi-document text generation in the field of science and technology intelligence, firstly, source data are acquired based on a topic crawler of an LDA topic similarity word library extension method; and sorting all text paragraphs through a text information value evaluation model of three indexes of authority, timeliness and content correlation of the text information. And selecting a paragraph with a higher score as an original text for generating the final science and technology intelligence. Finally, an unsupervised multi-document abstract method based on spectral clustering and multi-sentence compression is adopted, and a science and technology intelligence abstract is automatically generated. According to the method, the problem that in the data screening process, scientific and technological information generation has high requirements for data timeliness and authority is effectively solved, and the problem that a traditional multi-document generation method based on a neural network cannot be applied due to lack of a data set in the field of scientific and technological information is effectively solved.

The invention discloses a method and device for automatic generation of a multi-document abstract of industrial safety topics. The method includes the steps that input keywords are acquired, multiple documents corresponding to the keywords are inquired, and a document set is formed by the multiple documents; aiming at the documents in the document set, grammatical component decomposition is conducted on sentences in the documents, then multiple phrases are obtained, attribute information is set for each phrase, and a phrase set is formed by the multiple phrases; the phrase set is input into a sub-modular function, and the sub-modular function is optimized; according to an optimization result, a target phrase subset is determined, wherein the target phrase subset is a subset of the phrase set; multiple sentences are formed by phrases of the target phrase subset, the priority levels of the sentences are determined according to the attribute information of the phrases in the sentences; according to the priority levels of the sentences, the sentences are spliced in sequence, and the abstract is formed.