A multi-news timeline summarization algorithm based on aging theory

This paper focuses on the problem of news event timeline summary in Multi-Document Summarization, which aims to summarize multi-news regarding the same event in timeline. The majority of the traditional solutions to this problem consider the text surface features and topic-related features, such as the length of each sentence, the position of the sentence in the document, the number of topic words, etc. Traditional methods ignored that every event has its life circle including birth, growth, maturity and death. In this paper, a novel approach is presented for summarizing multi-news regarding the same topic in consideration of both the traditional features and the life circle feature of each event. The proposed approach consists of four steps. First, sentences and their publishing date are extracted from each news article. Second, the extracted sentences are pretreated to reduce the influence of noises like synonyms. Third, life circle features and other four categories of features which are common used in this field are collected. Finally, SVM model is used to train these features to recognize the summary sentence of the news document. This approach have been tested on the public datasets, DUC-2002 and TAC-2010, and the results show that our approach is more effective in summarizing multi-news in timeline than existing methods.