A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining (2020)

Abstract: With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems. However, the semantic structure and styles of meeting transcripts are quite different from articles and conversations. In this paper, we propose a novel abstractive summary network that adapts to the meeting scenario. We design a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers. Furthermore, due to the inadequacy of meeting summary data, we pretrain the model on large-scale news summary data. Empirical results show that our model outperforms previous approaches in both automatic metrics and human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases from 34.66% to 46.28%.

## introduction meetings are a very common forum where people exchange ideas, make plans, and share information. with the ubiquity of automatic speech recognition systems come vast amounts of meeting transcripts. therefore, the need to succinctly summarize the content of a meeting naturally arises. several methods of generating summaries for meetings have been proposed @xcite @xcite @xcite . as @xcite points out, users prefer abstractive meeting summaries to extractive summaries. while these methods are mostly abstractive, they require complicated multi-stage machine ## problem formulation we formalize the problem of meeting summarization as follows. the input consists of meeting transcripts x and meeting participants p. suppose there are s meetings in total. the tran-scripts are @xmath0, ..., x s }. each meeting transcript consists of multiple turns, where each turn is the utterance of a participant. thus, x @xmath1, u 1 ), (p 2 , u 2 ), ..., (p l i , u l i )}, where p j 2 p, 1  j  l i , is a participant and u @xmath2, ..., w l j ) is the tokenized utterance from p j . the human-labelled summary for meeting x i , denoted by y i , is also a sequence of tokens. for simplicity, we will drop the meeting index subscript. so the goal of the system is to generate meeting summary @xmath3, ..., y n ) given the transcripts @xmath4, u 1 ), (p 2 , u 2 ), ..., (p m , u m )}. ## model our hierarchical meeting summarization network (hmnet) is based on the encoder-decoder transformer structure @xcite , and its goal is to maximize the conditional probability of meeting summary y given transcript x and network parameters ✓: p (y |x; ✓). ## related work meeting summarization. there are a number of studies on generating summaries for meetings and dialogues @xcite @xcite . @xcite uses utterance clustering, an entailment graph, a semantic word graph and a ranking strategy to construct meeting summaries. @xcite and @xcite focus on various aspects of meetings such as decisions and action items. @xcite employs multi-sentence fusion to construct summarization templates for meetings, leading to summaries with higher readability and informativeness. recently, @xcite leverages a multisentence compression graph and budgeted submodular maximization to generate meeting summaries. in general, these multi-step methods make joint optimization intractable. @xcite proposes an encoder-decoder structure for end-to-end multimodal meeting summarization, but it depends on manual annotation of topic segmentation and visual focus, which may not be available in practice. in comparison, our model only requires meeting transcripts directly from speech recognition. ## conclusion in this paper, we present an end-to-end hierarchical neural network, hmnet, for abstractive meeting summarization. we employ a two-level hierarchical structure to adapt to the long meeting transcript, and a role vector to represent each participant. we also alleviate the data scarcity problem by pretraining on news summarization data. experiments show that hmnet achieves state-of-the-art performance in both automatic metrics and human evaluation. through an ablation study, we show that the role vector, hierarchical architecture, and pretraining all contribute to the model's performance. for future work, we plan to utilize organizational chart, knowledge graph and topic modeling to generate better meeting summaries, which can better