@inproceedings{kim-etal-2024-title,
    title = "Title-based Extractive Summarization via {MRC} Framework",
    author = "Kim, Hongjin  and
      Kim, Jai-Eun  and
      Kim, Harksoo",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1406",
    pages = "16175--16186",
    abstract = "Existing studies on extractive summarization have primarily focused on scoring and selecting summary sentences independently. However, these models are limited to sentence-level extraction and tend to select highly generalized sentences while overlooking the overall content of a document. To effectively consider the semantics of a document, in this study, we introduce a novel machine reading comprehension (MRC) framework for extractive summarization (MRCSum) by setting a query as the title. Our framework enables MRCSum to consider the semantic coherence and relevance of summary sentences in relation to the overall content. In particular, when a title is not available, we generate a title-like query, which is expected to achieve the same effect as a title. Our title-like query consists of the topic and keywords to serve as information on the main topic or theme of the document. We conduct experiments in both Korean and English languages, evaluating the performance of MRCSum on datasets comprising both long and short summaries. Our results demonstrate the effectiveness of MRCSum in extractive summarization, showcasing its ability to generate concise and informative summaries with or without explicit titles. Furthermore, our MRCSum outperforms existing models by capturing the essence of the document content and producing more coherent summaries.",
}

Existing studies on extractive summarization have primarily focused on scoring and selecting summary sentences independently. However, these models are limited to sentence-level extraction and tend to select highly generalized sentences while overlooking the overall content of a document. To effectively consider the semantics of a document, in this study, we introduce a novel machine reading comprehension (MRC) framework for extractive summarization (MRCSum) by setting a query as the title. Our framework enables MRCSum to consider the semantic coherence and relevance of summary sentences in relation to the overall content. In particular, when a title is not available, we generate a title-like query, which is expected to achieve the same effect as a title. Our title-like query consists of the topic and keywords to serve as information on the main topic or theme of the document. We conduct experiments in both Korean and English languages, evaluating the performance of MRCSum on datasets comprising both long and short summaries. Our results demonstrate the effectiveness of MRCSum in extractive summarization, showcasing its ability to generate concise and informative summaries with or without explicit titles. Furthermore, our MRCSum outperforms existing models by capturing the essence of the document content and producing more coherent summaries.
Kim, Hongjin Kim, Jai-Eun Kim, Harksoo
Title-based Extractive Summarization via MRC Framework