[论文解读] Automatic Text Summarization Methods: A Comprehensive Review
本文综述了最先进的文本摘要方法，覆盖抽取式和抽象式方法、数据集、评估指标，以及未来方向。
One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading. Simplifying the relevant information in the form of a summary will assist many people because the material on any topic is plentiful on the Internet. Manually summarising massive amounts of text is quite challenging for humans. So, it has increased the need for more complex and powerful summarizers. Researchers have been trying to improve approaches for creating summaries since the 1950s, such that the machine-generated summary matches the human-created summary. This study provides a detailed state-of-the-art analysis of text summarization concepts such as summarization approaches, techniques used, standard datasets, evaluation metrics and future scopes for research. The most commonly accepted approaches are extractive and abstractive, studied in detail in this work. Evaluating the summary and increasing the development of reusable resources and infrastructure aids in comparing and replicating findings, adding competition to improve the outcomes. Different evaluation methods of generated summaries are also discussed in this study. Finally, at the end of this study, several challenges and research opportunities related to text summarization research are mentioned that may be useful for potential researchers working in this area.
研究动机与目标
- 推动通过机器生成的摘要来应对信息过载的需求。
- 提供对文本摘要概念、方法和技术的详细前沿分析。
- 调研用于复制与比较的标准数据集、评估指标及相关基础设施。
- 识别文本摘要领域的挑战与未来研究机会。
提出的方法
- 将摘要分为抽取式和抽象式方法，并讨论它们的关键技术。
- 总结文本摘要中使用的技术、工作流程和核心概念。
- 回顾用于评估摘要质量的标准数据集和评估指标。
- 讨论可重复使用的资源和基础设施在实现复制和公平比较中的作用。
- 分析评估方法及其对该领域进展的影响。
实验结果
研究问题
- RQ1主要的摘要方法（抽取式与抽象式）及其技术是什么？
- RQ2用于评估摘要质量的标准数据集和评估指标是什么？
- RQ3当前文本摘要系统面临哪些挑战、有哪些未来研究机会？
- RQ4如何通过资源与基础设施改进结果的可复制性与可比性？
主要发现
- 提供对文本摘要概念与方法的详细前沿分析。
- 突出抽取式与抽象式摘要之间的区别及各自的技术。
- 讨论评估方法、数据集，以及可重复性资源的重要性。
- 概述推动未来摘要研究的挑战与研究机会。
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