Comparing Methods for Extractive Summarization of Call Centre Dialogue

September 06, 2022 ยท Declared Dead ยท ๐Ÿ› Artificial Intelligence and Applications

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Authors Alexandra N. Uma, Dmitry Sityaev arXiv ID 2209.02472 Category cs.CL: Computation & Language Cross-listed cs.AI Citations 1 Venue Artificial Intelligence and Applications Last Checked 6 months ago
Abstract
This paper provides results of evaluating some text summarisation techniques for the purpose of producing call summaries for contact centre solutions. We specifically focus on extractive summarisation methods, as they do not require any labelled data and are fairly quick and easy to implement for production use. We experimentally compare several such methods by using them to produce summaries of calls, and evaluating these summaries objectively (using ROUGE-L) and subjectively (by aggregating the judgements of several annotators). We found that TopicSum and Lead-N outperform the other summarisation methods, whilst BERTSum received comparatively lower scores in both subjective and objective evaluations. The results demonstrate that even such simple heuristics-based methods like Lead-N ca n produce meaningful and useful summaries of call centre dialogues.
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