Abstractive Summarization of Scientific Documents: Models and Evaluation Techniques-Reference-Cited by-同舟云学术

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Abstractive Summarization of Scientific Documents: Models and Evaluation Techniques

Published:2023-12-15 Issue: Volume: Page:121-124
ISSN:
Container-title:Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation
language:
Short-container-title:

Author:

Rehman Tohida¹^ORCID,Chattopadhyay Samiran²^ORCID,Sanyal Debarshi Kumar³^ORCID

Affiliation:

1. Information Technology, Jadavpur University, India

2. Techno India University, India and Information Technology, Jadavpur University, India

3. School of Mathematical & Computational Sciences, Indian Association for the Cultivation of Science, India

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3632754.3632771

Reference17 articles.

1. Satanjeev Banerjee and Alon Lavie. 2005. METEOR: An automatic metric for MT evaluation with improved correlation with human judgments. In Proc. of the ACL Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization. ACL, 65–72.

2. SciBERT: A Pretrained Language Model for Scientific Text

3. A Supervised Approach to Extractive Summarisation of Scientific Papers

4. Chin-Yew Lin. 2004. ROUGE: A package for automatic evaluation of summaries. In Text Summarization Branches Out. ACL, 74–81.

5. Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles

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