Query-Focused Multi-document Summarization Survey

With the exponential growth of textual information on web and in multimedia, query-focused multi-document summarization (QFMS) has emerged as a critical research area. QFMS aims to generate concise summaries that address user queries satisfy their needs. This paper provides comprehensive survey state-of-the-art approaches QFMS, focusing specifically graph-based clustering-based methods. Each approach is examined detail, highlighting its advantages disadvantages. The covers ranking algorithms, sentence selection techniques, redundancy removal methods, evaluation metrics, available datasets. principal aim this present thorough analysis approaches, providing researchers practitioners with valuable insights into field. By surveying existing identifies challenges issues faced discusses potential future directions. Moreover, emphasizes importance addressing coherency, ambiguity, vague references, redundancy, diversity QFMS. Performance standards competing are also discussed, showcasing advancements made acknowledges need for improving coherence, readability, semantic efficiency, while balancing compression ratios summarizing quality. Additionally, it highlights hybrid methods integration extractive abstractive techniques achieve more human-like summaries.