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. The paper provided 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, seventeen evaluation metrics used for evaluating algorithms for text summaries against the human-curated summaries are compiled here in this paper. Extensive experiments are performed on 8 different data sets. Comparative results of 9 methodologies, each representing one of the 6 different groups, are presented. Seven different evaluation metrics are used in the comparative study. It is observed that DL and ML based QMDS methods are performing better in comparison to the other methods.

ACM Computing Survey
Suman Kundu
Suman Kundu
Assistant Professor of Computer Science and Engineering

My research interests include social network analysis, network data science, streaming algorithms, big data, granular computing, soft computing, fuzzy and rough sets.