An Exploratory Study on Long Dialogue Summarization: What Works and What's Next
This addresses the problem of efficiently summarizing lengthy conversations for applications like meetings and interviews, but it is incremental as it builds on existing methods.
The study tackled the challenge of summarizing long dialogues that exceed input limits of transformer models by comparing three strategies, finding that retrieve-then-summarize pipeline models performed best on datasets like QMSum, MediaSum, and SummScreen, with improvements from stronger retrieval and pretraining.
Dialogue summarization helps readers capture salient information from long conversations in meetings, interviews, and TV series. However, real-world dialogues pose a great challenge to current summarization models, as the dialogue length typically exceeds the input limits imposed by recent transformer-based pre-trained models, and the interactive nature of dialogues makes relevant information more context-dependent and sparsely distributed than news articles. In this work, we perform a comprehensive study on long dialogue summarization by investigating three strategies to deal with the lengthy input problem and locate relevant information: (1) extended transformer models such as Longformer, (2) retrieve-then-summarize pipeline models with several dialogue utterance retrieval methods, and (3) hierarchical dialogue encoding models such as HMNet. Our experimental results on three long dialogue datasets (QMSum, MediaSum, SummScreen) show that the retrieve-then-summarize pipeline models yield the best performance. We also demonstrate that the summary quality can be further improved with a stronger retrieval model and pretraining on proper external summarization datasets.