Emergent event abstract extracting method based on sparse learning

Abstract

The invention discloses an emergent event abstract extracting method based on a sparse learning theory. The method comprises the following steps: acquiring emergent event topics, and performing query extension on the emergent event topics to obtain an extension topic lexical item set of the event topics; retrieving events according to extension topic lexical items after the query extension of the events to obtain document sets relevant to the event topics; performing feature selection and semantic clustering on the document sets of the topics in sequence by a non-negative matrix decomposing method to obtain topic clustering results of the events; and extracting a representative sentence from the clustering results to serve as a finial abstract result according to a maximal marginal relevance (MMR) method. Through the technical scheme provided by the invention, when the emergent events burst, latest condition information of event development is provided for a user in the presence of massive redundant news report streams.

Description

technical field [0001] The invention belongs to the field of text information processing, and relates to a method for extracting emergency abstracts based on sparse learning. Background technique [0002] Living in the information age, it is no longer a problem for users to obtain massive reports on events and topics they are interested in. However, big data does not mean big knowledge, especially when emergencies occur, and the number of related event reports explodes. In this way, how to efficiently, timely and conveniently track the development status of specific topic emergencies from the massive news data stream with geometric growth, and finally form a summary of event development that is easy for readers to read, so as to help people learn from numerous It has become a very urgent task to quickly obtain the latest development status of emergencies that one is interested in in news reports. [0003] Most of the traditional multi-document summarization methods filter ...

AI Technical Summary

Problems solved by technology

However, this method of summarization does not solve the problem from the essence of the data set - in the face of massive redundant news data streams, finding related events on a specific topic is like finding a needle in a haystack, and the number of topics in the same period is staggering, so it must be taken Effective feature extraction method, select the topic set that can reflect the minimum redundancy that can represent events in this period

In addition, traditional abstract extraction techniques often ignore the semantic relationship between news text data samples, and only focus on the score of a single sentence. In this way, only the sentence with the highest score is extracted as the final abstract sentence. Although the accuracy of a single sentence is high, it is often In general, descriptions of emergencies are often insufficiently comprehensive or redundant

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