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Rouge: A package for automatic evaluation of summaries. In Text summarization branches out . 74–81
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Summarization of spontaneous conversations. In Ninth International Conference on Spoken Language Processing
Original
Xiaodan Zhu and Gerald Penn. 2006 · 2008
Earlier work this paper cites.
Topic-focused summarization of chat conversations. In European Conference on Information Retrieval . Springer, 800–803
Arpit Sood, Thanvir P Mohamed, and Vasudeva Varma. 2013 · 2013
Earlier work this paper cites.
A neural attention model for abstractive sentence summarization
Original
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Earlier work this paper cites.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Original
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al · 2016
Earlier work this paper cites.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.

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Multimodal machine learning: A survey and taxonomy
Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency. 2018 · 2018
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Bigpatent: A large-scale dataset for abstractive and coherent summarization
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Eva Sharma, Chen Li, and Lu Wang. 2019 · 2019
Later among the works it cites.
Longformer: The long-document transformer
Original
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2020
Closest in time.
The Spotify Podcasts Dataset
Original
Ann Clifton, Aasish Pappu, Sravana Reddy, Yongze Yu, Jussi Karlgren, Ben Carterette, and Rosie Jones. 2020 · 2020
Closest in time.
Reformer: The efficient transformer
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2020

A Baseline Analysis for Podcast Abstractive Summarization

Zheng, Chujie, Wang, Harry Jiannan, Zhang, Kunpeng et al.

Understand

Podcast summary, an important factor affecting end-users' listening decisions, has often been considered a critical feature in podcast recommendation systems, as well as many downstream applications.

Existing abstractive summarization approaches are mainly built on fine-tuned models on professionally edited texts such as CNN and DailyMail news.
Different from news, podcasts are often longer, more colloquial and conversational, and noisier with contents on commercials and sponsorship, which makes automatic podcast summarization extremely challenging.
This paper presents a baseline analysis of podcast summarization using the Spotify Podcast Dataset provided by TREC 2020.

Built on

Rouge: A package for automatic evaluation of summaries. In Text summarization branches out . 74–81
Chin-Yew Lin. 2004 · 2004
Earlier work this paper cites.
Summarization of spontaneous conversations. In Ninth International Conference on Spoken Language Processing
Original
Xiaodan Zhu and Gerald Penn. 2006 · 2008
Earlier work this paper cites.
Topic-focused summarization of chat conversations. In European Conference on Information Retrieval . Springer, 800–803
Arpit Sood, Thanvir P Mohamed, and Vasudeva Varma. 2013 · 2013
Earlier work this paper cites.
A neural attention model for abstractive sentence summarization
Original
Alexander M Rush, Sumit Chopra, and Jason Weston. 2015 · 2015
Earlier work this paper cites.
Abstractive text summarization using sequence-to-sequence rnns and beyond
Original
Ramesh Nallapati, Bowen Zhou, Caglar Gulcehre, Bing Xiang, et al · 2016
Earlier work this paper cites.
Attention is all you need. In Advances in neural information processing systems . 5998–6008
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Earlier work this paper cites.

Similar

Multimodal machine learning: A survey and taxonomy
Tadas Baltrušaitis, Chaitanya Ahuja, and Louis-Philippe Morency. 2018 · 2018
Cited alongside, same era.
A discourse-aware attention model for abstractive summarization of long documents
Original
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018 · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Original
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Abstractive summarization of spoken and written conversation
Original
Prakhar Ganesh and Saket Dingliwal. 2019 · 2019
Cited alongside, same era.
Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension

Then

Bigpatent: A large-scale dataset for abstractive and coherent summarization
Original
Eva Sharma, Chen Li, and Lu Wang. 2019 · 2019
Later among the works it cites.
Longformer: The long-document transformer
Original
Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020 · 2020
Closest in time.
The Spotify Podcasts Dataset
Original
Ann Clifton, Aasish Pappu, Sravana Reddy, Yongze Yu, Jussi Karlgren, Ben Carterette, and Rosie Jones. 2020 · 2020
Closest in time.
Reformer: The efficient transformer
Original
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya. 2020 · 2020
Closest in time.
Screenplay Summarization Using Latent Narrative Structure
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov, and Luke Zettlemoyer. 2019 · 2019

Cited alongside, same era.

Exploring the limits of transfer learning with a unified text-to-text transformer

Original

Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu. 2019 · 2019

Cited alongside, same era.

Pinelopi Papalampidi, Frank Keller, Lea Frermann, and Mirella Lapata. 2020 · 2020

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Prophetnet: Predicting future n-gram for sequence-to-sequence pre-training

Original

Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang, and Ming Zhou. 2020 · 2020

Closest in time.