Abstract
Multi-document summarization (MDS) is the task of reflecting key points from any set of documents into a concise text paragraph. In the past, it has been used to aggregate news, tweets, product reviews, etc. from various sources. Owing to no standard definition of the task, we encounter a plethora of datasets with varying levels of overlap and conflict between participating documents. There is also no standard regarding what constitutes summary information in MDS. Adding to the challenge is the fact that new systems report results on a set of chosen datasets, which might not correlate with their performance on the other datasets. In this paper, we study this heterogeneous task with the help of a few widely used MDS corpora and a suite of state-of-theart models. We make an attempt to quantify the quality of summarization corpus and prescribe a list of points to consider while proposing a new MDS corpus. Next, we analyze the reason behind the absence of an MDS system which achieves superior performance across all corpora. We then observe the extent to which system metrics are influenced, and bias is propagated due to corpus properties. The scripts to reproduce the experiments in this work are available at https://github.com/LCS2-IIITD/summarization_bias.git- Anthology ID:
- 2020.findings-emnlp.254
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2020
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Trevor Cohn, Yulan He, Yang Liu
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2830–2840
- Language:
- URL:
- https://aclanthology.org/2020.findings-emnlp.254
- DOI:
- 10.18653/v1/2020.findings-emnlp.254
- Cite (ACL):
- Alvin Dey, Tanya Chowdhury, Yash Kumar, and Tanmoy Chakraborty. 2020. Corpora Evaluation and System Bias Detection in Multi-document Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2830–2840, Online. Association for Computational Linguistics.
- Cite (Informal):
- Corpora Evaluation and System Bias Detection in Multi-document Summarization (Dey et al., Findings 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.findings-emnlp.254.pdf
- Code
- LCS2-IIITD/summarization_bias
- Data
- CQASUMM, Multi-News