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Towards Economical Long-Form Summarization: A Chunk-Based Approach Using LLMs
Research Article | Published: 17 November 2025
ICCK Transactions on Large Language Models Volume 1, Issue 1, 2025: 4-8
Volume 1, Issue 1, ICCK Transactions on Large Language Models
Volume 1, Issue 1, 2025
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ICCK Transactions on Large Language Models, Volume 1, Issue 1, 2025: 4-8

Free to Read | Research Article | 17 November 2025
Towards Economical Long-Form Summarization: A Chunk-Based Approach Using LLMs
by
Avishto Banerjee Avishto Banerjee 1 *
1 SAP Labs India Pvt. Ltd., Bengaluru 560066, India
* Corresponding Author: Avishto Banerjee, [email protected]
DOI: 10.62762/TLLM.2025.674475
Received: 27 April 2025, Accepted: 25 July 2025, Published: 17 November 2025  
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Abstract
In today's world anything almost everything related to literature can be achieved by LLMs. Be it summarization, abstraction, translation, transformation, etc. But not always is it possible to do those operations on extremely large content. Even with the large token output limits of newly launched advanced LLMs it is not always economically and technically feasible to perform such operations. To cater to such a problem this paper explores the idea of summarization of extensive contents by a chunk-based approach which is both efficient and economical. This approach also understands the drawback of loss of information while chunking and efficiently solves that issue. The usage of such a framework is highly demandable across various enterprise software industries as well as healthcare and financial industries to store, summarize as well as query various large contents which are sometimes challenging to maintain and query. To create a generic framework the approach used for the summarization is mainly zero-shot summarization.
Graphical Abstract
Towards Economical Long-Form Summarization: A Chunk-Based Approach Using LLMs
Keywords
LLMs
summarization
chunking
generative AI
NLP
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
Avishto Banerjee is an employee of SAP Labs India Pvt. Ltd., Bengaluru 560066, India. The author declares no conflicts of interest. 
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Banerjee, A. (2025). Towards Economical Long-Form Summarization: A Chunk-Based Approach Using LLMs. ICCK Transactions on Large Language Models, 1(1), 4–8. https://doi.org/10.62762/TLLM.2025.674475
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