Extractive summarization of multi-party meetings through discourse segmentation

Abstract

In this article we tackle the problem of multi-party conversation summarization. We investigate the role of discourse segmentation of a conversation on meeting summarization. First, an unsupervised function segmentation algorithm is proposed to segment the transcript into functionally coherent parts, such asMonologuei(which indicates a segment where speakeriis the dominant speaker, e.g., lecturing all the other participants) orDiscussionx1x2, . . .,xn(which indicates a segment where speakersx1toxninvolve in a discussion). Then the salience score for a sentence is computed by leveraging the score of the segment containing the sentence. Performance of our proposed segmentation and summarization algorithms is evaluated using the AMI meeting corpus. We show better summarization performance over other state-of-the-art algorithms according to different metrics.

Joint Modeling of Content and Discourse Relations in Dialogues

“…Our model is inspired by research work that leverages discourse structure for identifying salient content in conversations, which is still largely reliant on features derived from gold-standard discourse labels (McKeown et al, 2007;Murray et al, 2010;Bokaei et al, 2016). For instance, adjacency pairs, which are paired utterances with question-answer or offer-accept relations, are found to frequently appear in meeting summaries together and thus are utilized to extract summary-worthy utterances by Galley (2006).…”

Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation

“…Text segmentation deals with automatically breaking down the structure of text into such topically contiguous segments, i.e., it aims to identify the points of topic shift (Hearst 1994;Choi 2000;Brants, Chen, and Tsochantaridis 2002;Riedl and Biemann 2012;Du, Buntine, and Johnson 2013;Glavaš, Nanni, and Ponzetto 2016;Koshorek et al 2018). Reliable segmentation results with texts that are more readable for humans, but also facilitates downstream tasks like automated text summarization (Angheluta, De Busser, and Moens 2002;Bokaei, Sameti, and Liu 2016), passage retrieval (Huang et al 2003;Shtekh et al 2018), topical classification (Zirn et al 2016), or dialog modeling (Manuvinakurike et al 2016;Zhao and Kawahara 2017).…”

A Joint Model of Conversational Discourse and Latent Topics on Microblogs

“…Zhang et al (2013) and Li et al (2015a) leverage dialogue acts to indicate summary-worthy messages. In the fields conversation summarization from other domains, e.g., meetings, forums, and emails, it is also popular to leverage the pre-detected discourse structure for summarization (Murray et al 2006;Wang and Cardie 2013;Bhatia, Biyani, and Mitra 2014;McKeown, Shrestha, and Rambow 2007;Bokaei, Sameti, and Liu 2016). Oya and Carenini (2014) and Qin, Wang, and Kim (2017) address discourse tagging together with salient content discovery on emails and meetings, and show the usefulness of their relations in summarization.…”