  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.
