A Hybrid Strategy for Chat Transcript Summarization

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) deve...

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Main Author: Pratik K. Biswas
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/10705279/
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author Pratik K. Biswas
author_facet Pratik K. Biswas
author_sort Pratik K. Biswas
collection DOAJ
description 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 (unsupervised) and abstractive (supervised) summarization techniques in compressing ill-punctuated or unpunctuated 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.
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spelling doaj-art-8dfdb075d82942239b2f6cfa04c50dc82025-08-20T01:47:50ZengIEEEIEEE Access2169-35362024-01-011214662014663410.1109/ACCESS.2024.347396810705279A Hybrid Strategy for Chat Transcript SummarizationPratik K. Biswas0https://orcid.org/0000-0001-8570-3108Artificial Intelligence and Data Center, Verizon Communications, Basking Ridge, NJ, USAText 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 (unsupervised) and abstractive (supervised) summarization techniques in compressing ill-punctuated or unpunctuated 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.https://ieeexplore.ieee.org/document/10705279/Extractive summarizationabstractive summarizationtopic modelingtransformerstransfer learningreinforcement learning
spellingShingle Pratik K. Biswas
A Hybrid Strategy for Chat Transcript Summarization
IEEE Access
Extractive summarization
abstractive summarization
topic modeling
transformers
transfer learning
reinforcement learning
title A Hybrid Strategy for Chat Transcript Summarization
title_full A Hybrid Strategy for Chat Transcript Summarization
title_fullStr A Hybrid Strategy for Chat Transcript Summarization
title_full_unstemmed A Hybrid Strategy for Chat Transcript Summarization
title_short A Hybrid Strategy for Chat Transcript Summarization
title_sort hybrid strategy for chat transcript summarization
topic Extractive summarization
abstractive summarization
topic modeling
transformers
transfer learning
reinforcement learning
url https://ieeexplore.ieee.org/document/10705279/
work_keys_str_mv AT pratikkbiswas ahybridstrategyforchattranscriptsummarization
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