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        <title>Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS)</title>
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    <abstract>It is well recognized that creating summaries of scientific texts can be difficult. For each given document, the majority of summarizing research believes there is only one best gold summary. Having just one gold summary limits our capacity to assess the effectiveness of summarizing algorithms because creating summaries is an art. Likewise, because it takes subject-matter experts a lot of time to read and comprehend lengthy scientific publications, annotating several gold summaries for scientific documents can be very expensive. The shared task known as the Multi perspective Scientific Document Summarization (Mup) is an exploration of various methods to produce multi perspective scientific summaries. Utilizing Graph Attention Networks (GATs), we take an extractive text summarization approach to the issue as a kind of sentence ranking task. Although the results produced by the suggested model are not particularly impressive, comparing them to the state-of-the-arts demonstrates the model’s potential for improvement.</abstract>
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%0 Conference Proceedings
%T Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS)
%A Akkasi, Abbas
%Y Cohan, Arman
%Y Feigenblat, Guy
%Y Freitag, Dayne
%Y Ghosal, Tirthankar
%Y Herrmannova, Drahomira
%Y Knoth, Petr
%Y Lo, Kyle
%Y Mayr, Philipp
%Y Shmueli-Scheuer, Michal
%Y de Waard, Anita
%Y Wang, Lucy Lu
%S Proceedings of the Third Workshop on Scholarly Document Processing
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F akkasi-2022-multi
%X It is well recognized that creating summaries of scientific texts can be difficult. For each given document, the majority of summarizing research believes there is only one best gold summary. Having just one gold summary limits our capacity to assess the effectiveness of summarizing algorithms because creating summaries is an art. Likewise, because it takes subject-matter experts a lot of time to read and comprehend lengthy scientific publications, annotating several gold summaries for scientific documents can be very expensive. The shared task known as the Multi perspective Scientific Document Summarization (Mup) is an exploration of various methods to produce multi perspective scientific summaries. Utilizing Graph Attention Networks (GATs), we take an extractive text summarization approach to the issue as a kind of sentence ranking task. Although the results produced by the suggested model are not particularly impressive, comparing them to the state-of-the-arts demonstrates the model’s potential for improvement.
%U https://aclanthology.org/2022.sdp-1.33
%P 268-272

Markdown (Informal)

[Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS)](https://aclanthology.org/2022.sdp-1.33) (Akkasi, sdp 2022)

• Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS) (Akkasi, sdp 2022)

ACL

• Abbas Akkasi. 2022. Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS). In Proceedings of the Third Workshop on Scholarly Document Processing, pages 268–272, Gyeongju, Republic of Korea. Association for Computational Linguistics.