Single document summarization using the information from documents with the same topic

• Published in: Knowledge-based systems 2021-09, Vol.228, p.107265, Article 107265
• Main Authors: Mao, Xiangke, Huang, Shaobin, Shen, Linshan, Li, Rongsheng, Yang, Hui
• Format: Article
• Language: English
• Subjects: Algorithms; Biased LexRank; Extractive summarization; Graph model; Graphical representations; Neighborhood documents; Random walk
• ISSN: 0950-7051; 1872-7409
• DOI: 10.1016/j.knosys.2021.107265

The essence of extractive summarization is to measure the importance of sentences in the document. When extracting summary from a single document, it is difficult to comprehensively and effectively evaluate the importance of sentences due to the lack of information. In this paper, we propose a kind of single document summarization method using information from documents under the same topic. This method integrates the topic information from neighborhood documents and statistical information from the target document to calculate the score of sentences. Then the scoring results are used as a prior scores for each sentence in the target document. After the target document is represented by the sentence graph, the final score of the sentences are obtained by the biased random walk algorithm. Finally, the Maximal Marginal Relevance (MMR) algorithm is used to select the sentences to form summary. The experimental results on the DUC2001 and DUC2002 datasets show that the effect of extracting summary is improved by incorporating information from the documents under the same topic.