面向人民日报语料的新闻自动摘要生成

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

[目的/意义]面向主流新闻媒体人民日报语料展开研究,旨在为文本自动摘要研究提供思路和实践支撑,进而应用到新闻等相关文本信息处理中,为知识聚合服务和信息获取途径研究做出贡献。[方法/过程]以新时代人民日报语料NEPD中的2015年1月、2015年6月和2016年1月的人民日报分词语料作为实验语料,基于TF-IDF、Textrank等抽取式自动摘要算法,以及基于指针生成网络的生成式自动摘要模型展开研究,并对摘要结果进行分析评价。[结果/结论]实验设计面向人民日报语料的新闻抽取式自动摘要算法,构建面向人民日报语料的新闻生成式自动摘要指针生成网络模型,并通过Rouge指标(包括Rouge-1、Rouge-2和Rouge-L 3种指标)对实验结果进行评测,为人民日报分词语料的应用提供具体思路,并对新闻自动摘要系统研究提供语料支持和实践支撑。

[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.