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arXiv · 2406.17526

LumberChunker: Long-Form Narrative Document Segmentation

André V. Duarte·João Marques·Miguel Graça·Miguel Freire·Lei Li·Arlindo L. Oliveira
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Abstract

Modern NLP tasks increasingly rely on dense retrieval methods to access up-to-date and relevant contextual information. We are motivated by the premise that retrieval benefits from segments that can vary in size such that a content's semantic independence is better captured. We propose LumberChunker, a method leveraging an LLM to dynamically segment documents, which iteratively prompts the LLM to identify the point within a group of sequential passages where the content begins to shift. To evaluate our method, we introduce GutenQA, a benchmark with 3000 "needle in a haystack" type of question-answer pairs derived from 100 public domain narrative books available on Project Gutenberg. Our experiments show that LumberChunker not only outperforms the most competitive baseline by 7.37% in retrieval performance (DCG@20) but also that, when integrated into a RAG pipeline, LumberChunker proves to be more effective than other chunking methods and competitive baselines, such as the Gemini 1.5M Pro. Our Code and Data are available at https://github.com/joaodsmarques/LumberChunker

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André V. Duarte, João Marques, Miguel Graça, Miguel Freire, Lei Li, Arlindo L. Oliveira. 2024-06-25. LumberChunker: Long-Form Narrative Document Segmentation. https://arxiv.org/abs/2406.17526

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