Deep Learning Models for Automatic Summarization

Lemberger, Pirmin

doi:10.48550/arxiv.2005.11988

Cited by 1 publication

(1 citation statement)

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“…Zhang et al [39] proposed HIBERT for document encoding, designed a method for pre-training it for document modeling using unlabeled data and then applied their pre-trained HIBERT to document summarization to achieve state-of-the-art performance on both CNN/Dailymail and New York Times datasets. Lemberger et al [40] reviewed several deep learning architectures for automatic text summarization. Our approach is semantically similar to [18], [22] and [23], but the differences lie in our contributions, as listed in Section III.…”

Section: Related Workmentioning

confidence: 99%

Extractive Summarization of Call Transcripts

Biswas

Iakubovich

2022

IEEE Access

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Automatic text summarization is one of the most challenging and interesting problems in natural language processing (NLP). Text summarization is the process of extracting the most important information from the text and presenting it concisely in fewer sentences. Call transcript involves textual description of a phone conversation between a customer (caller) and agent(s) (customer representatives). Call transcripts pose unique challenges that are not adequately addressed by most open-source automatic text summarizers, which are developed to summarize continuous texts such as articles and stories. This paper presents an indigenously developed method that combines topic modeling and sentence selection with punctuation restoration in condensing ill-punctuated or un-punctuated call transcripts to produce more readable summaries. This unique combination is what distinguishes the proposed summarizer from other text summarizers. Extensive testing, evaluation and comparisons, with an open-source, state-of-the-art extractive summarizer using three different pre-trained language models, have demonstrated the efficacy of this summarizer for call transcript summarization. The summaries generated by the proposed summarizer were shown to be more compelling and useful based on multiple criteria.

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