A Text Summarization Model and Automatic Text Summarization Method Based on Improved Selection Mechanism and LSTM Variation

Abstract

The invention discloses a text summarization model based on an improved selection mechanism and LSTM variant and an automatic text summarization method. On the basis of an encoder-decoder model based on an attention mechanism, the invention proposes a selection mechanism based on information gain and Copy-based LSTM variant. On the one hand, an improved selection mechanism is added between the encoder and the decoder to judge the key information in the original text and extract the summary information, which improves the generalization ability of automatic text summarization; on the other hand, the LSTM variant is used as the The recurrent unit of the decoder-side recurrent neural network can optimize the decoding process, improve the decoding efficiency, reduce the repetition problem in the generated summary and improve the readability of the generated summary.

Description

technical field [0001] The present invention relates to the technical field of artificial intelligence and natural language processing, in particular to a text summarization model and text summarization method based on an improved selection mechanism and LSTM variant. Background technique [0002] With the rapid development of the Internet, text data such as news, blogs, and emails in the Internet are flooding our lives, and there are often redundant and useless information in these text data. In this Internet big data era of information explosion, how to retrieve useful information from a large amount of text data is a very challenging task. Through short summaries, we can efficiently retrieve text content and mine text information. The title of the article can be sensational and not worthy of the name, but the abstract of the article must be in line with the central idea and content of the article. Manually writing summaries for each article, news, blog, and email will c...

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