Query-Focused Multi-document Summarization Survey

Abstract

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