@inproceedings{kumar-etal-2022-team,
title = "Team {AINLPML} @ {M}u{P} in {SDP} 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach",
author = "Kumar, Sandeep and
Kohli, Guneet Singh and
Shinde, Kartik and
Ekbal, Asif",
editor = "Cohan, Arman and
Feigenblat, Guy and
Freitag, Dayne and
Ghosal, Tirthankar and
Herrmannova, Drahomira and
Knoth, Petr and
Lo, Kyle and
Mayr, Philipp and
Shmueli-Scheuer, Michal and
de Waard, Anita and
Wang, Lucy Lu",
booktitle = "Proceedings of the Third Workshop on Scholarly Document Processing",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.sdp-1.36",
pages = "285--290",
abstract = "This paper introduces the proposed summarization system of the AINLPML team for the First Shared Task on Multi-Perspective Scientific Document Summarization at SDP 2022. We present a method to produce abstractive summaries of scientific documents. First, we perform an extractive summarization step to identify the essential part of the paper. The extraction step includes utilizing a contributing sentence identification model to determine the contributing sentences in selected sections and portions of the text. In the next step, the extracted relevant information is used to condition the transformer language model to generate an abstractive summary. In particular, we fine-tuned the pre-trained BART model on the extracted summary from the previous step. Our proposed model successfully outperformed the baseline provided by the organizers by a significant margin. Our approach achieves the best average Rouge F1 Score, Rouge-2 F1 Score, and Rouge-L F1 Score among all submissions.",
}
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%0 Conference Proceedings
%T Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach
%A Kumar, Sandeep
%A Kohli, Guneet Singh
%A Shinde, Kartik
%A Ekbal, Asif
%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 kumar-etal-2022-team
%X This paper introduces the proposed summarization system of the AINLPML team for the First Shared Task on Multi-Perspective Scientific Document Summarization at SDP 2022. We present a method to produce abstractive summaries of scientific documents. First, we perform an extractive summarization step to identify the essential part of the paper. The extraction step includes utilizing a contributing sentence identification model to determine the contributing sentences in selected sections and portions of the text. In the next step, the extracted relevant information is used to condition the transformer language model to generate an abstractive summary. In particular, we fine-tuned the pre-trained BART model on the extracted summary from the previous step. Our proposed model successfully outperformed the baseline provided by the organizers by a significant margin. Our approach achieves the best average Rouge F1 Score, Rouge-2 F1 Score, and Rouge-L F1 Score among all submissions.
%U https://aclanthology.org/2022.sdp-1.36
%P 285-290
Markdown (Informal)
[Team AINLPML @ MuP in SDP 2021: Scientific Document Summarization by End-to-End Extractive and Abstractive Approach](https://aclanthology.org/2022.sdp-1.36) (Kumar et al., sdp 2022)
ACL