Abstractive Meeting Summarization: A Survey

TL;DR

This survey reviews recent advances in abstractive meeting summarization, highlighting challenges, datasets, models, and evaluation metrics enabled by deep learning and encoder-decoder architectures.

Contribution

It provides a comprehensive overview of the current state, challenges, and methodologies in abstractive meeting summarization research.

Findings

Deep learning has significantly advanced language generation for summarization.

Encoder-decoder architectures are particularly effective for multi-party conversations.

Various datasets and evaluation metrics are used to benchmark progress.

Abstract

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.