Towards Abstractive Grounded Summarization of Podcast Transcripts
Kaiqiang Song, Chen Li, Xiaoyang Wang, Dong Yu, Fei Liu
Abstract
Podcasts have shown a recent rise in popularity. Summarization of podcasts is of practical benefit to both content providers and consumers. It helps people quickly decide whether they will listen to a podcast and/or reduces the cognitive load of content providers to write summaries. Nevertheless, podcast summarization faces significant challenges including factual inconsistencies of summaries with respect to the inputs. The problem is exacerbated by speech disfluencies and recognition errors in transcripts of spoken language. In this paper, we explore a novel abstractive summarization method to alleviate these issues. Our approach learns to produce an abstractive summary while grounding summary segments in specific regions of the transcript to allow for full inspection of summary details. We conduct a series of analyses of the proposed approach on a large podcast dataset and show that the approach can achieve promising results. Grounded summaries bring clear benefits in locating the summary and transcript segments that contain inconsistent information, and hence improve summarization quality in terms of automatic and human evaluation.
BibTeX
@inproceedings{song-etal-2022-towards,
title = "Towards Abstractive Grounded Summarization of Podcast Transcripts",
author = "Song, Kaiqiang and
Li, Chen and
Wang, Xiaoyang and
Yu, Dong and
Liu, Fei",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.302/",
doi = "10.18653/v1/2022.acl-long.302",
pages = "4407--4418"
}