A Hybrid Strategy for Chat Transcript Summarization
This work addresses the challenge of summarizing customer-agent chat transcripts for businesses, but it is incremental as it builds on existing summarization techniques.
The paper tackled the problem of summarizing unpunctuated chat transcripts by developing a hybrid method combining extractive and abstractive techniques with reinforcement learning, resulting in improved readability and quality for large-scale deployment without needing annotated summaries.
Text summarization is the process of condensing a piece of text to fewer sentences, while still preserving its content. Chat transcript, in this context, is a textual copy of a digital or online conversation between a customer (caller) and agent(s). This paper presents an indigenously (locally) developed hybrid method that first combines extractive and abstractive summarization techniques in compressing ill-punctuated or un-punctuated chat transcripts to produce more readable punctuated summaries and then optimizes the overall quality of summarization through reinforcement learning. Extensive testing, evaluations, comparisons, and validation have demonstrated the efficacy of this approach for large-scale deployment of chat transcript summarization, in the absence of manually generated reference (annotated) summaries.