DialogSum: A Real-Life Scenario Dialogue Summarization Dataset
This dataset addresses the problem of summarizing real-life dialogues for applications such as customer service, but it is incremental as it builds on existing summarization research.
The authors introduced DialogSum, a large-scale dataset for dialogue summarization, and found that existing neural summarizers face unique challenges like spoken terms and discourse structures, requiring specialized representation learning.
Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of real-life scenarios including customer service management and medication tracking. To this end, we propose DialogSum, a large-scale labeled dialogue summarization dataset. We conduct empirical analysis on DialogSum using state-of-the-art neural summarizers. Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.