Enhancing Stand-Up Comedy summarization using cohesion and coherence with deep learning

Supriyono, Supriyono ORCID: https://orcid.org/0000-0002-4733-9189, Wibawa, Aji Prasetya, Suyono, Suyono and Kurniawan, Fachrul ORCID: https://orcid.org/0000-0002-3709-8764 (2026) Enhancing Stand-Up Comedy summarization using cohesion and coherence with deep learning. JOIV : International Journal on Informatics Visualization, 10 (1). pp. 57-65. ISSN 2549-9610 (print) | 2549-9904 (online)

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Abstract

Summarizing stand-up comedy scripts presents a unique problem because of the complex interplay of language, timing, and cultural references that define humor. Traditional summarization techniques sometimes fail to preserve the punchlines, irony, and narrative coherence essential to comedic storytelling. This study introduces a hybrid methodology that integrates linguistic cohesion and coherence methodologies with a deep learning framework employing Long Short-Term Memory (LSTM) networks and Word2Vec embeddings. Our objective is to develop a summarization model that captures both the structural coherence and humorous essence of stand-up performances in a more succinct format. We employed a bilingual dataset of English and Indonesian comic scripts for text preprocessing, embedding, and sequence modelling during system training. The assessment employed metrics such as accuracy, precision, recall, and F1-score, together with a qualitative analysis of the generated summaries. The results demonstrate that the proposed methodology significantly improves humor retention and logical coherence compared to conventional methods. The accuracy increased from 0.65 to 0.83 after training, and validation measures confirmed strong generalization. This approach offers considerable promise for content regulation, customized recommendations, and AI-augmented entertainment solutions. This research improves natural language comprehension in informal, culturally diverse genres by preserving the distinctive structure and intent of comedic language.

Item Type:	Journal Article
Keywords:	Stand-up comedy; text summarization; humor retention; cohesion and coherence; natural language processing
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080107 Natural Language Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Supriyono Supriyono
Date Deposited:	07 Apr 2026 15:36

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