Towards Abstractive Grounded Summarization of Podcast Transcripts
Towards Abstractive Grounded Summarization of Podcast Transcripts
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DOI：
10.48550/arxiv.2203.11425
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Kaiqiang Song;Chen Li;Xiaoyang Wang;Dong Yu;Fei Liu
中科院分区：
文献类型：
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作者：
Kaiqiang Song;Chen Li;Xiaoyang Wang;Dong Yu;Fei Liu
播客最近越来越受欢迎。播客的总结对于内容提供商和消费者来说都有实际好处。它可以帮助人们快速决定是否收听播客和/或减少内容提供商撰写摘要的认知负担。然而，播客摘要面临着重大挑战，包括摘要与输入的事实不一致。口语转录中的言语不流畅和识别错误加剧了这个问题。在本文中，我们探索了一种新颖的抽象概括方法来缓解这些问题。我们的方法学习生成抽象摘要，同时将摘要片段放置在成绩单的特定区域，以便全面检查摘要细节。我们在大型播客数据集上对所提出的方法进行了一系列分析，并表明该方法可以取得有希望的结果。接地摘要在定位包含不一致信息的摘要和转录片段方面带来了明显的好处，从而提高了自动和人工评估方面的摘要质量。
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.