Comparing Methods for Extractive Summarization of Call Centre Dialogue
원문 초록
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.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SafeChat: A Framework for Building Trustworthy Collaborative Assistants and a Case Study of its Usefulness
OrderSum: Semantic Sentence Ordering for Extractive Summarization
A Novel Word Pair-based Gaussian Sentence Similarity Algorithm For Bengali Extractive Text Summarization
Multi-label Sequential Sentence Classification via Large Language Model
Fair Summarization: Bridging Quality and Diversity in Extractive Summaries
Towards Enhancing Coherence in Extractive Summarization: Dataset and Experiments with LLMs
토론
이 논문과 연결된 포럼 글이 아직 없습니다.