Nerve network modeling method and device used for forming title

Abstract

The embodiment of the invention discloses a nerve network modeling method and device used for forming titles; the method comprises the following steps: obtaining a plurality of sample documents and standard titles corresponding to each sample document; inputting each sample document into a to-be-optimized model, and allowing the to-be-optimized model to output reference titles corresponding to each sample document according to the received sample documents; coupling the reference titles with standard titles corresponding to each sample document, thus obtaining semantic gap between the reference title and the standard title; optimizing target parameters in the to-be-optimized model according to the semantic gaps corresponding to each sample document. The method and device can optimize the parameters in the to-be-optimized model in a sentence level according to the semantic gaps between the reference titles and the standard titles; compared with a conventional method capable of optimizing the to-be-optimized model only in the vocabulary level, the nerve network modeling method and device can improve model stability, and can improve the title forming accuracy.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of natural language processing and automatic text summarization, and in particular to a neural network modeling method and device for generating headlines. Background technique [0002] Automatic summarization refers to the use of computers to automatically obtain accurate and coherent summaries that can cover the core ideas of the source text. Automatic text summarization technology is mainly divided into two categories: extraction and summarization. The former directly selects some sentences from the original document and combines them as an abstract. Although it has the characteristics of simplicity, it cannot give a concise and coherent abstract within a shorter length limit. In contrast, abstractive summarization attempts to understand the semantics of the original document, thereby generating a digest whose sentences do not exactly come from the original document. When the abstr...

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Problems solved by technology

[0005] One purpose of the embodiments of the present invention is to solve the problem that the existing neural network model is only optimized at the word level, resulting in low accuracy of the generated titles

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