mRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images

Keighley Overbay, Jaewoo Ahn, Fatemeh Pesaran zadeh, Joonsuk Park, Gunhee Kim


Abstract
The growing number of multimodal online discussions necessitates automatic summarization to save time and reduce content overload. However, existing summarization datasets are not suitable for this purpose, as they either do not cover discussions, multiple modalities, or both. To this end, we present mRedditSum, the first multimodal discussion summarization dataset. It consists of 3,033 discussion threads where a post solicits advice regarding an issue described with an image and text, and respective comments express diverse opinions. We annotate each thread with a human-written summary that captures both the essential information from the text, as well as the details available only in the image. Experiments show that popular summarization models—GPT-3.5, BART, and T5—consistently improve in performance when visual information is incorporated. We also introduce a novel method, cluster-based multi-stage summarization, that outperforms existing baselines and serves as a competitive baseline for future work.
Anthology ID:
2023.emnlp-main.251
Volume:
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4117–4132
Language:
URL:
https://aclanthology.org/2023.emnlp-main.251
DOI:
10.18653/v1/2023.emnlp-main.251
Bibkey:
Cite (ACL):
Keighley Overbay, Jaewoo Ahn, Fatemeh Pesaran zadeh, Joonsuk Park, and Gunhee Kim. 2023. mRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 4117–4132, Singapore. Association for Computational Linguistics.
Cite (Informal):
mRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images (Overbay et al., EMNLP 2023)
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PDF:
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