  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.
