Abstractive Meeting Summarization: A Survey
This is a survey paper, providing an overview for researchers and practitioners interested in abstractive summarization of meetings, but it is incremental as it synthesizes existing work without novel contributions.
The paper surveys the challenges, datasets, models, and evaluation metrics for abstractive meeting summarization, which aims to generate concise summaries from multi-party conversations, but does not present new experimental results or concrete numbers.
A system that could reliably identify and sum up the most important points of a conversation would be valuable in a wide variety of real-world contexts, from business meetings to medical consultations to customer service calls. Recent advances in deep learning, and especially the invention of encoder-decoder architectures, has significantly improved language generation systems, opening the door to improved forms of abstractive summarization, a form of summarization particularly well-suited for multi-party conversation. In this paper, we provide an overview of the challenges raised by the task of abstractive meeting summarization and of the data sets, models and evaluation metrics that have been used to tackle the problems.