SDbQfSum: Query-focused summarization framework basedon diversity and text semantic analysis

Muhidin Mohamed*, Mourad Oussalah, Victor Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Query-focused multi-document summarization (Qf-MDS) is a sub-task of automatic text summarization that aims to extract a substitute summary from a document cluster of the same topic and based on a user query. Unlike other summarization tasks, Qf-MDS has specific research challenges including the differences and similarities across related document sets, the high degree of redundancy inherent in the summaries created from multiple related sources, relevance to the given query, topic diversity in the produced summary and the small source-to-summary compression ratio. In this work, we propose a semantic diversity feature based query-focused extractive summarizer (SDbQfSum) built on powerful text semantic representation techniques underpinned with Wikipedia commonsense knowledge in order to address the query-relevance, centrality, redundancy and diversity challenges. Specifically, a semantically parsed document text is combined with knowledge-based vectorial representation to extract effective sentence importance and query-relevance features. The proposed monolingual summarizer is evaluated on a standard English dataset for automatic query-focused summarization tasks, that is, the DUC2006 dataset. The obtained results show that our summarizer outperforms most state-of-the-art related approaches on one or more ROUGE measures achieving 0.418, 0.092 and 0.152 in ROUGE-1, ROUGE-2,and ROUGE-SU4 respectively. It also attains competitive performance with the slightly outperforming system(s), for example, the difference between our system's result and best system in ROUGE-1 is just 0.006. We also found through the conducted experiments that our proposed custom cluster merging algorithm significantly reduces information redundancy while maintaining topic diversity across documents.
Original languageEnglish
Article numbere13462
Number of pages20
JournalExpert Systems
Issue number1
Early online date29 Sept 2023
Publication statusPublished - Jan 2024

Bibliographical note

© 2023 The Authors. Expert Systems published by John Wiley & Sons Ltd.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.


  • query-focused summarization
  • query-relevance
  • semantic role labeling
  • sentence centrality
  • sentence similarity


Dive into the research topics of 'SDbQfSum: Query-focused summarization framework basedon diversity and text semantic analysis'. Together they form a unique fingerprint.

Cite this