AroundConnect

2020

A Two-Phase Approach for Abstractive Podcast Summarization

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

Understand

Podcast summarization is different from summarization of other data formats, such as news, patents, and scientific papers in that podcasts are often longer, conversational, colloquial, and full of sponsorship and advertising information, which imposes great challenges for existing models.

  • In this paper, we focus on abstractive podcast summarization and propose a two-phase approach: sentence selection and seq2seq learning.
  • Specifically, we first select important sentences from the noisy long podcast transcripts.
  • The selection is based on sentence similarity to the reference to reduce the redundancy and the associated latent topics to preserve semantics.

Built on

  • Latent dirichlet allocation

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    Earlier work this paper cites.

  • 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.

  • 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.

  • Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension

    Original

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

    Earlier work this paper cites.

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