Extractive Summarization of Call Transcripts
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 des...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9946852/ |
| _version_ | 1797986993744904192 |
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| author | Pratik K. Biswas Aleksandr Iakubovich |
| author_facet | Pratik K. Biswas Aleksandr Iakubovich |
| author_sort | Pratik K. Biswas |
| collection | DOAJ |
| description | 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 are shown to be more compelling and useful based on multiple criteria. |
| first_indexed | 2024-04-11T07:41:50Z |
| format | Article |
| id | doaj.art-7c7d94444a704d3ab888bdf69c13bd16 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:41:50Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7c7d94444a704d3ab888bdf69c13bd162022-12-22T04:36:30ZengIEEEIEEE Access2169-35362022-01-011011982611984010.1109/ACCESS.2022.32214049946852Extractive Summarization of Call TranscriptsPratik K. Biswas0https://orcid.org/0000-0001-8570-3108Aleksandr Iakubovich1https://orcid.org/0000-0002-2287-8039Artificial Intelligence and Data, Global Network and Technology (GNT), Verizon Communications, Basking Ridge, NJ, USACore Engineering and Operations, Global Network and Technology (GNT), Verizon Communications, Richardson, TX, USAAutomatic 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 are shown to be more compelling and useful based on multiple criteria.https://ieeexplore.ieee.org/document/9946852/Extractive summarizationtopic modelstransformersembeddingpunctuation restoration |
| spellingShingle | Pratik K. Biswas Aleksandr Iakubovich Extractive Summarization of Call Transcripts IEEE Access Extractive summarization topic models transformers embedding punctuation restoration |
| title | Extractive Summarization of Call Transcripts |
| title_full | Extractive Summarization of Call Transcripts |
| title_fullStr | Extractive Summarization of Call Transcripts |
| title_full_unstemmed | Extractive Summarization of Call Transcripts |
| title_short | Extractive Summarization of Call Transcripts |
| title_sort | extractive summarization of call transcripts |
| topic | Extractive summarization topic models transformers embedding punctuation restoration |
| url | https://ieeexplore.ieee.org/document/9946852/ |
| work_keys_str_mv | AT pratikkbiswas extractivesummarizationofcalltranscripts AT aleksandriakubovich extractivesummarizationofcalltranscripts |