论文标题
抽象播客摘要的两阶段方法
A Two-Phase Approach for Abstractive Podcast Summarization
论文作者
论文摘要
播客摘要与其他数据格式的摘要不同，例如新闻，专利和科学论文，播客通常更长，对话，通俗性，并充满赞助和广告信息，这对现有模型构成了巨大挑战。在本文中，我们专注于抽象播客摘要，并提出了一种两相方法：句子选择和SEQ2SEQ学习。具体来说，我们首先从嘈杂的长播客成绩单中选择重要句子。选择基于与参考的句子相似性，以减少冗余和相关的潜在主题，以保留语义。然后，将选定的句子送入摘要生成的预训练的编码器框架中。我们的方法在基于胭脂的措施和人类评估方面取得了令人鼓舞的结果。
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. Then the selected sentences are fed into a pre-trained encoder-decoder framework for the summary generation. Our approach achieves promising results regarding both ROUGE-based measures and human evaluations.
