Automatic Summary Generation of News for People’s Daily Online Corpus

Abstract: [Purpose/significance] This paper conducts a study for the mainstream news media for People’s Daily Online corpus, aiming to provide ideas and practical support for the study of automatic text summarization, which can then be applied to news and other related text information processing, and contribute to knowledge aggregation services and information access research. [Method/process] The experimental corpus of this research was the sub-corpus of the People’s Daily Online in January 2015, June 2015 and January 2016 in the new era People’s Daily (NEPD). Based on TF-IDF, Textrank and other extractive automatic summarization algorithms, based on the generative automatic abstractive summarization model for the pointer-generator network, the research was carried out and analyzed and evaluated the summarization results. [Result/conclusion] The experiment builds a news extraction automatic abstractive algorithm the Pointer-Generator Networks model for the People’s Daily corpus, and constructs a network model of news generative automatic summary pointer generation for People’s Daily Online corpus. Fruitful experimental results are evaluated by Rouge indicator (including 3 indicators: Rouge-1, Rouge-2 and Rouge-L). This article provides corpus support and practical support for the automatic news summarization system.