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        <title>Keyphrase Extraction from Scientific Articles via Extractive Summarization</title>
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        <namePart type="given">Giorgos</namePart>
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    <abstract>Automatically extracting keyphrases from scholarly documents leads to a valuable concise representation that humans can understand and machines can process for tasks, such as information retrieval, article clustering and article classification. This paper is concerned with the parts of a scientific article that should be given as input to keyphrase extraction methods. Recent deep learning methods take titles and abstracts as input due to the increased computational complexity in processing long sequences, whereas traditional approaches can also work with full-texts. Titles and abstracts are dense in keyphrases, but often miss important aspects of the articles, while full-texts on the other hand are richer in keyphrases but much noisier. To address this trade-off, we propose the use of extractive summarization models on the full-texts of scholarly documents. Our empirical study on 3 article collections using 3 keyphrase extraction methods shows promising results.</abstract>
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    <identifier type="doi">10.18653/v1/2021.sdp-1.6</identifier>
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%0 Conference Proceedings
%T Keyphrase Extraction from Scientific Articles via Extractive Summarization
%A Kontoulis, Chrysovalantis Giorgos
%A Papagiannopoulou, Eirini
%A Tsoumakas, Grigorios
%S Proceedings of the Second Workshop on Scholarly Document Processing
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F kontoulis-etal-2021-keyphrase
%X Automatically extracting keyphrases from scholarly documents leads to a valuable concise representation that humans can understand and machines can process for tasks, such as information retrieval, article clustering and article classification. This paper is concerned with the parts of a scientific article that should be given as input to keyphrase extraction methods. Recent deep learning methods take titles and abstracts as input due to the increased computational complexity in processing long sequences, whereas traditional approaches can also work with full-texts. Titles and abstracts are dense in keyphrases, but often miss important aspects of the articles, while full-texts on the other hand are richer in keyphrases but much noisier. To address this trade-off, we propose the use of extractive summarization models on the full-texts of scholarly documents. Our empirical study on 3 article collections using 3 keyphrase extraction methods shows promising results.
%R 10.18653/v1/2021.sdp-1.6
%U https://aclanthology.org/2021.sdp-1.6
%U https://doi.org/10.18653/v1/2021.sdp-1.6
%P 49-55

Markdown (Informal)

[Keyphrase Extraction from Scientific Articles via Extractive Summarization](https://aclanthology.org/2021.sdp-1.6) (Kontoulis et al., sdp 2021)

• Keyphrase Extraction from Scientific Articles via Extractive Summarization (Kontoulis et al., sdp 2021)

ACL

• Chrysovalantis Giorgos Kontoulis, Eirini Papagiannopoulou, and Grigorios Tsoumakas. 2021. Keyphrase Extraction from Scientific Articles via Extractive Summarization. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 49–55, Online. Association for Computational Linguistics.