How Domain Terminology Affects Meeting Summarization Performance

Proceedings of the 28th International Conference on Computational Linguistics, pages 5689–5695 Barcelona, Spain (Online), December 8-13, 2020 5689 How Domain Terminology Affects Meeting Summarization Performance Jia Jin Koay, Alexander Roustai, Xiaojin Dai, Dillon Burns, Alec Kerrigan, Fei Liu Computer Science Department, University of Central Florida Orlando, FL 32816, USA {jjkoay,alexroustai,xd.zangyiwu,aleckerrigan}@knights.ucf.edu feiliu@cs.ucf.edu Abstract Meetings are essential to modern organizations. Numerous meetings are held and recorded daily, more than can ever be comprehended. A meeting summarization system that identifies salient utterances from the transcripts to automatically generate meeting minutes can help. It empowers users to rapidly search and sift through large meeting collections. To date, the impact of domain terminology on the performance of meeting summarization remains understudied, despite that meetings are rich with domain knowledge. In this paper, we create gold-standard annotations for domain terminology on a sizable meeting corpus; they are known as jargon terms. We then analyze the performance of a meeting summarization system with and without jargon terms. Our findings reveal that domain terminology can have a substantial impact on summarization performance. We publicly release all domain terminology to advance research in meeting summarization.1 1 Introduction A vast number of meetings are being held and recorded everyday, far more than can ever be comprehended. With this explosion of meetings comes a pressing need to develop summarization techniques to assist in browsing meeting archives (Carletta et al., 2006; Ailomaa et al., 2006). A meeting summarization system takes a meeting recording and its transcript as input and produces a concise text summary as output, which preserves the most important content of the meeting discussion (Murray and Carenini, 2008; Liu and Liu, 2009; Shang et al., 2018; Li et al., 2019). The techniques hold great potential to make large archives of meetings substantially more efficient to browse, search and facilitate information sharing. We envision an automated summarizer that is capable of generating meeting minutes by identifying salient utterances from transcribed meeting recordings. Neural text summarization has seen significant progress (See et al., 2017; Tan et al., 2017; Chen and Bansal, 2018; Narayan et al., 2018; Lebanoff et al., 2018; West et al., 2019; Liu and Lapata, 2019; Laban et al., 2020), but most prior work focused on written texts. In contrast, recent years have seen a growing interest in summarizing spoken texts (Tardy et al., 2020). Particularly, the characteristics of meetings, domain terminology and limited annotated data pose novel challenges to neural summarization models. We favor extractive over abstractive models as the latter are prone to hallucinate content that is unfaithful to the input (Kryscinski et al., 2019). In this paper, we investigate how domain terminology impacts meeting summarization performance, especially in the context of neural extractive summarization. Jargon is the specialized terminology associated with a particular domain (Meyers et al., 2014). It is employed in a communicative context and may not be well understood outside that context. Because meetings are usually held among professionals, jargon is ubiquitous in meeting discussions. In Table 1, we provide an example of jargon terms …

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