@inproceedings{cho-etal-2021-streamhover,
title = "{S}tream{H}over: Livestream Transcript Summarization and Annotation",
author = "Cho, Sangwoo and
Dernoncourt, Franck and
Ganter, Tim and
Bui, Trung and
Lipka, Nedim and
Chang, Walter and
Jin, Hailin and
Brandt, Jonathan and
Foroosh, Hassan and
Liu, Fei",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.520",
doi = "10.18653/v1/2021.emnlp-main.520",
pages = "6457--6474",
abstract = "With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge. However, the problem is nontrivial due to the informal nature of spoken language. Further, there has been a shortage of annotated datasets that are necessary for transcript summarization. In this paper, we present StreamHover, a framework for annotating and summarizing livestream transcripts. With a total of over 500 hours of videos annotated with both extractive and abstractive summaries, our benchmark dataset is significantly larger than currently existing annotated corpora. We explore a neural extractive summarization model that leverages vector-quantized variational autoencoder to learn latent vector representations of spoken utterances and identify salient utterances from the transcripts to form summaries. We show that our model generalizes better and improves performance over strong baselines. The results of this study provide an avenue for future research to improve summarization solutions for efficient browsing of livestreams.",
}
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<abstract>With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge. However, the problem is nontrivial due to the informal nature of spoken language. Further, there has been a shortage of annotated datasets that are necessary for transcript summarization. In this paper, we present StreamHover, a framework for annotating and summarizing livestream transcripts. With a total of over 500 hours of videos annotated with both extractive and abstractive summaries, our benchmark dataset is significantly larger than currently existing annotated corpora. We explore a neural extractive summarization model that leverages vector-quantized variational autoencoder to learn latent vector representations of spoken utterances and identify salient utterances from the transcripts to form summaries. We show that our model generalizes better and improves performance over strong baselines. The results of this study provide an avenue for future research to improve summarization solutions for efficient browsing of livestreams.</abstract>
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%0 Conference Proceedings
%T StreamHover: Livestream Transcript Summarization and Annotation
%A Cho, Sangwoo
%A Dernoncourt, Franck
%A Ganter, Tim
%A Bui, Trung
%A Lipka, Nedim
%A Chang, Walter
%A Jin, Hailin
%A Brandt, Jonathan
%A Foroosh, Hassan
%A Liu, Fei
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F cho-etal-2021-streamhover
%X With the explosive growth of livestream broadcasting, there is an urgent need for new summarization technology that enables us to create a preview of streamed content and tap into this wealth of knowledge. However, the problem is nontrivial due to the informal nature of spoken language. Further, there has been a shortage of annotated datasets that are necessary for transcript summarization. In this paper, we present StreamHover, a framework for annotating and summarizing livestream transcripts. With a total of over 500 hours of videos annotated with both extractive and abstractive summaries, our benchmark dataset is significantly larger than currently existing annotated corpora. We explore a neural extractive summarization model that leverages vector-quantized variational autoencoder to learn latent vector representations of spoken utterances and identify salient utterances from the transcripts to form summaries. We show that our model generalizes better and improves performance over strong baselines. The results of this study provide an avenue for future research to improve summarization solutions for efficient browsing of livestreams.
%R 10.18653/v1/2021.emnlp-main.520
%U https://aclanthology.org/2021.emnlp-main.520
%U https://doi.org/10.18653/v1/2021.emnlp-main.520
%P 6457-6474
Markdown (Informal)
[StreamHover: Livestream Transcript Summarization and Annotation](https://aclanthology.org/2021.emnlp-main.520) (Cho et al., EMNLP 2021)
ACL
- Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, and Fei Liu. 2021. StreamHover: Livestream Transcript Summarization and Annotation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6457–6474, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.