Automatic text summarization: What has been done and what has to be done

Aries, Abdelkrime; Zegour, Djamel eddine; Hidouci, Walid Khaled

doi:10.48550/arxiv.1904.00688

Cited by 2 publications

(3 citation statements)

References 77 publications

(116 reference statements)

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“…One of the main drawbacks of the graph-based approach is that it doesn't consider the semantic structure of the sentence [29]. However, some researchers tried to handle this issue by taking into account additional language-dependent parallels, like semantic similarity [30]. Abdi et al [31] also proposed a query-based summary method that combines sentiment analysis and summarization approaches.…”

Section: ) Graph-based Approachmentioning

confidence: 99%

Query-Focused Multi-document Summarization Survey

Alanzi,

Alballaa

2023

IJACSA

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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.

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Section: ) Graph-based Approachmentioning

confidence: 99%

Query-Focused Multi-document Summarization Survey

Alanzi,

Alballaa

2023

IJACSA

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“…We also adopt a selection verification mechanism to allow the authors of the paper to review the selected methods. Some survey works [30][31][32] are referenced to confirm that relevant approaches were not missed.…”

Section: Efficiency (Answering Rq4)mentioning

confidence: 99%

“…The study concluded that text summarization had made significant progress employing deep learning, however, the application of various deep learning architectures, parameters, and their respective impacts on results are yet to be analyzed. Aries et al30 reviewed different works of automatic text summarization and presented some problems and limits that prevent works from moving forward. In this paper, different classifications of automated text summarization are reviewed, which will help create a novel summary approach based on existing method classifications.…”

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confidence: 99%

Are duplicates really harmful? An empirical study on bug report summarization techniques

Hao

Feng

et al. 2022

J Software Evolu Process

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Recent research works have proven that duplicate bug reports can provide helpful information to assist developers in software tasks such as fault localization and program fixing, while thoroughly reading duplicate bug reports is time‐consuming and inefficient. Summarization is a possible solution for gaining essential information quickly. However, there are many challenges when applying existing summarizing techniques on duplicate bug reports. Duplicate bug reports describe the same problem from different views and vary in quality, content, and writing style. Moreover, the code snippet understanding and the semantic gap between natural and programming languages make the summary generation even more difficult. Thus, in this paper, we want to investigate whether the state‐of‐the‐art summarization approaches can overcome the resistance and generate an effective summary for duplicate bug reports. We collected more than 8,000 groups of duplicate reports from GitHub and labeled 60 groups with 149 reports manually for the evaluation. Results showed that although the existing summarization approaches can work on duplicate bug reports, there are significant differences between them when it comes to code snippet summarization. Moreover, several methods can be very sluggish for summarizing long bug reports. Our study provides insights and guidelines for choosing proper summarization approaches in different scenarios.

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Automatic text summarization: What has been done and what has to be done

Cited by 2 publications

References 77 publications

Query-Focused Multi-document Summarization Survey

Query-Focused Multi-document Summarization Survey

Are duplicates really harmful? An empirical study on bug report summarization techniques

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