@inproceedings{overbay-etal-2023-mredditsum,
title = "m{R}eddit{S}um: A Multimodal Abstractive Summarization Dataset of {R}eddit Threads with Images",
author = "Overbay, Keighley and
Ahn, Jaewoo and
Pesaran zadeh, Fatemeh and
Park, Joonsuk and
Kim, Gunhee",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.251",
doi = "10.18653/v1/2023.emnlp-main.251",
pages = "4117--4132",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T mRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images
%A Overbay, Keighley
%A Ahn, Jaewoo
%A Pesaran zadeh, Fatemeh
%A Park, Joonsuk
%A Kim, Gunhee
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F overbay-etal-2023-mredditsum
%X 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.
%R 10.18653/v1/2023.emnlp-main.251
%U https://aclanthology.org/2023.emnlp-main.251
%U https://doi.org/10.18653/v1/2023.emnlp-main.251
%P 4117-4132
Markdown (Informal)
[mRedditSum: A Multimodal Abstractive Summarization Dataset of Reddit Threads with Images](https://aclanthology.org/2023.emnlp-main.251) (Overbay et al., EMNLP 2023)
ACL