DialogSum Challenge: Summarizing Real-Life Scenario Dialogues
Yulong Chen, Yang Liu, Yue Zhang
Abstract
We propose a shared task on summarizing real-life scenario dialogues, DialogSum Challenge, to encourage researchers to address challenges in dialogue summarization, which has been less studied by the summarization community. Real-life scenario dialogue summarization has a wide potential application prospect in chat-bot and personal assistant. It contains unique challenges such as special discourse structure, coreference, pragmatics, and social common sense, which require specific representation learning technologies to deal with. We carefully annotate a large-scale dialogue summarization dataset based on multiple public dialogue corpus, opening the door to all kinds of summarization models.
Topics & Concepts
Automatic summarizationComputer scienceCoreferenceTask (project management)Representation (politics)PragmaticsNatural language processingMulti-document summarizationWorld Wide WebArtificial intelligenceData scienceInformation retrievalLinguisticsResolution (logic)PoliticsEconomicsLawManagementPhilosophyPolitical scienceTopic ModelingNatural Language Processing TechniquesSpeech and dialogue systems