Review on Query-focused Multi-document Summarization (QMDS) with Comparative Analysis

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.