DialogSum: A Real-Life Scenario Dialogue Summarization Dataset

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 reallife 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.

(a) Dialogue from DIALOGSUM: #Person_1#: Good morning. I wonder whether you have got an answer from your superior. #Person_2#: Yes, we had a meting about it yesterday afternoon. #Person_1#: What's the answer? #Person_2#: We decided that we could agree to your price, but we are a bit worried about the slow delivery. #Person_1#: Let me see. I quoted your delivery in three months, didn't I? #Person_2#: Yes, but we hope that the wool could reach us as soon as possible. #Person_1#: I thought you would. So I rang Auckland last night. As you are our biggest customer, they agreed to ship the order on the first vessel available that will leave Auckland next month. #Person_2#: Good, if you agree we'll draft the agreement right away and sign it then. #Person_1#: By all means.