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Multi Perspective Scientific Document Summarization With Graph Attention Networks (GATS)
(Association for Computational Linguistics (ACL), 2022) Akkasi, Abbas
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 with the state-of-the-arts demonstrates the model’s potential for improvement. © 2022 COLING. All Rights Reserved.