Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization (2012.03502v2)

Abstract: Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data. Existing methods view the meeting as a linear sequence of utterances while ignoring the diverse relations between each utterance. Besides, the limited labeled data further hinders the ability of data-hungry neural models. In this paper, we try to mitigate the above challenges by introducing dialogue-discourse relations. First, we present a Dialogue Discourse-Dware Meeting Summarizer (DDAMS) to explicitly model the interaction between utterances in a meeting by modeling different discourse relations. The core module is a relational graph encoder, where the utterances and discourse relations are modeled in a graph interaction manner. Moreover, we devise a Dialogue Discourse-Aware Data Augmentation (DDADA) strategy to construct a pseudo-summarization corpus from existing input meetings, which is 20 times larger than the original dataset and can be used to pretrain DDAMS. Experimental results on AMI and ICSI meeting datasets show that our full system can achieve SOTA performance. Our codes will be available at: https://github.com/xcfcode/DDAMS.

Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization

Overview

The paper, "Dialogue Discourse-Aware Graph Model and Data Augmentation for Meeting Summarization," addresses critical challenges in the domain of abstractive meeting summarization, namely the dynamic interaction among multiple speakers and the paucity of labeled training data. The authors propose a novel approach that incorporates dialogue discourse relations to enhance meeting modeling and introduces a data augmentation strategy to expand the training dataset significantly. The paper claims state-of-the-art (SOTA) performance on recognized meeting datasets by using these methods.

Proposed Methodology

The core innovation of the paper is the Dialogue Discourse-Aware Meeting Summarizer (DDAMS) which utilizes a relational graph model for encoding interactions within meeting utterances. This model diverges from traditional linear sequence modeling by introducing a graph-based approach that explicitly models discourse relations. The framework captures the complex interplay typical in multiparty meetings by converting utterances and discourse relations into a graph structure using a relational graph encoder.

The model further employs a Dialogue Discourse-Aware Data Augmentation (DDDA) strategy. This strategy constructs pseudo-summarization data from existing meeting transcripts, leveraging the identifying 'QA' discourse relations to formulate pseudo-meetings and pseudo-summaries. The augmented data significantly enhances the pretraining phase, presenting a pseudo corpus that is 20 times larger than the original dataset.

Experimental Results

Extensive experimentation on the AMI and ICSI meeting corpora demonstrates the efficacy of the DDAMS. The model achieves superior performance in ROUGE metrics compared to several baselines such as TextRank, Pointer-Generator, HRED, and HMNet. Specifically, results on the AMI dataset show improvements with ROUGE-1 at 53.15, ROUGE-2 at 22.32, and ROUGE-L at 25.67 after pretraining, which represents a significant advancement compared to existing methods.

Implications

This research has profound implications for both theoretical and practical dimensions of AI-driven summarization tasks. The graph-based model with discourse-aware capabilities challenges conventional sequential document representation, offering a robust mechanism for capturing multi-speaker dynamics in a conversation. The discourse-driven data augmentation strategy provides a framework to tackle data sparsity, thus enabling more comprehensive training of neural summarization models.

From a practical standpoint, the ability to generate high-quality summaries from meeting transcripts can enhance productivity and information management in organizational settings, facilitating efficient decision-making processes.

Future Directions

The use of dialogue discourse relations presents promising future research avenues. Enhancing the quality and accuracy of discourse parsers could further improve model performance. Additionally, expanding this graph model framework to other domains like conversational AI or chatbots might yield interesting extensions. The authors’ graph-based learning approach could inspire new architectures in dialogue systems, potentially revolutionizing the way conversations are understood and summarized.

In conclusion, the paper provides valuable insights by leveraging dialogue discourse relations for meeting summarization and sets a benchmark for future research in discourse-aware AI systems.