自动汇总
计算机科学
情报检索
多文档摘要
查询扩展
Web搜索查询
数据挖掘
搜索引擎
作者
Pratap Roy,Suman Kundu
摘要
The problem of query-focused multi-document summarization (QMDS) is to generate a summary from multiple source documents on identical/similar topics based on the query submitted by the users. This article provides a systematic review of the literature of QMDS. The research works are classified into six major categories based on the summarization methodologies used. Different techniques used for finding query-relevant summaries for different algorithms under each of the six major groups are reported. Further, 17 evaluation metrics used for evaluating algorithms for text summaries against the human-curated summaries are compiled here in this article. Extensive experiments are performed on eight different datasets. Comparative results of nine methodologies, each representing one of the six different groups, are presented. Seven different evaluation metrics are used in the comparative study. It is observed that DL- and ML-based QMDS methods perform. better in comparison to the other methods.
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