A Hybrid Strategy for Chat Transcript Summarization
- URL: http://arxiv.org/abs/2402.01510v2
- Date: Wed, 31 Jul 2024 03:57:33 GMT
- Title: A Hybrid Strategy for Chat Transcript Summarization
- Authors: Pratik K. Biswas,
- Abstract summary: Sumomarization is the process of condensing a piece of text to fewer sentences, while still preserving its content.
This paper presents an indigenously developed hybrid method that combines extractive and abstractive summarization techniques.
Tests, evaluations, comparisons, and validation have demonstrated the efficacy of this approach for large-scale deployment of chat transcript summarization.
- Score: 3.0567294793102784
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Aspect-based Meeting Transcript Summarization: A Two-Stage Approach with
Weak Supervision on Sentence Classification [91.13086984529706]
Aspect-based meeting transcript summarization aims to produce multiple summaries.
Traditional summarization methods produce one summary mixing information of all aspects.
We propose a two-stage method for aspect-based meeting transcript summarization.
arXiv Detail & Related papers (2023-11-07T19:06:31Z) - Rank Your Summaries: Enhancing Bengali Text Summarization via
Ranking-based Approach [0.0]
This paper aims to identify the most accurate and informative summary for a given text by utilizing a simple but effective ranking-based approach.
We utilize four pre-trained summarization models to generate summaries, followed by applying a text ranking algorithm to identify the most suitable summary.
Experimental results suggest that by leveraging the strengths of each pre-trained transformer model, our methodology significantly improves the accuracy and effectiveness of the Bengali text summarization.
arXiv Detail & Related papers (2023-07-14T15:07:20Z) - Factually Consistent Summarization via Reinforcement Learning with
Textual Entailment Feedback [57.816210168909286]
We leverage recent progress on textual entailment models to address this problem for abstractive summarization systems.
We use reinforcement learning with reference-free, textual entailment rewards to optimize for factual consistency.
Our results, according to both automatic metrics and human evaluation, show that our method considerably improves the faithfulness, salience, and conciseness of the generated summaries.
arXiv Detail & Related papers (2023-05-31T21:04:04Z) - NapSS: Paragraph-level Medical Text Simplification via Narrative
Prompting and Sentence-matching Summarization [46.772517928718216]
We propose a summarize-then-simplify two-stage strategy, which we call NapSS.
NapSS identifies the relevant content to simplify while ensuring that the original narrative flow is preserved.
Our model achieves significantly better than the seq2seq baseline on an English medical corpus.
arXiv Detail & Related papers (2023-02-11T02:20:25Z) - A General Contextualized Rewriting Framework for Text Summarization [15.311467109946571]
Exiting rewriting systems take each extractive sentence as the only input, which is relatively focused but can lose necessary background knowledge and discourse context.
We formalize contextualized rewriting as a seq2seq with group-tag alignments, identifying extractive sentences through content-based addressing.
Results show that our approach significantly outperforms non-contextualized rewriting systems without requiring reinforcement learning.
arXiv Detail & Related papers (2022-07-13T03:55:57Z) - SNaC: Coherence Error Detection for Narrative Summarization [73.48220043216087]
We introduce SNaC, a narrative coherence evaluation framework rooted in fine-grained annotations for long summaries.
We develop a taxonomy of coherence errors in generated narrative summaries and collect span-level annotations for 6.6k sentences across 150 book and movie screenplay summaries.
Our work provides the first characterization of coherence errors generated by state-of-the-art summarization models and a protocol for eliciting coherence judgments from crowd annotators.
arXiv Detail & Related papers (2022-05-19T16:01:47Z) - A Survey on Neural Abstractive Summarization Methods and Factual
Consistency of Summarization [18.763290930749235]
summarization is the process of shortening a set of textual data computationally, to create a subset (a summary)
Existing summarization methods can be roughly divided into two types: extractive and abstractive.
An extractive summarizer explicitly selects text snippets from the source document, while an abstractive summarizer generates novel text snippets to convey the most salient concepts prevalent in the source.
arXiv Detail & Related papers (2022-04-20T14:56:36Z) - Controllable Abstractive Dialogue Summarization with Sketch Supervision [56.59357883827276]
Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score.
arXiv Detail & Related papers (2021-05-28T19:05:36Z) - Extractive Summarization of Call Transcripts [77.96603959765577]
This paper presents an indigenously developed method that combines topic modeling and sentence selection with punctuation restoration in ill-punctuated or un-punctuated call transcripts.
Extensive testing, evaluation and comparisons have demonstrated the efficacy of this summarizer for call transcript summarization.
arXiv Detail & Related papers (2021-03-19T02:40:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.