Hierarchical Text Segmentation

Enhancing Retrieval Augmented Generation with Hierarchical Text Segmentation Chunking

Retrieval-Augmented Generation (RAG) systems commonly use chunking strategies for retrieval, which enhance large language models (LLMs) by enabling them to access external knowledge, ensuring that the retrieved informati…

TocBERT: Medical Document Structure Extraction Using Bidirectional Transformers

Text segmentation holds paramount importance in the field of Natural Language Processing (NLP). It plays an important role in several NLP downstream tasks like information retrieval and document summarization. In this wo…

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

The Segment Anything Model (SAM), a profound vision foundation model pretrained on a large-scale dataset, breaks the boundaries of general segmentation and sparks various downstream applications. This paper introduces Hi…

Training and Domain Adaptation for Supervised Text Segmentation

Unlike traditional unsupervised text segmentation methods, recent supervised segmentation models rely on Wikipedia as the source of large-scale segmentation supervision. These models have, however, predominantly been eva…

Hierarchical Text Segmentation for Medieval Manuscripts

In this paper, we address the segmentation of books of hours, Latin devotional manuscripts of the late Middle Ages, that exhibit challenging issues: a complex hierarchical entangled structure, variable content, noisy tra…

C-HTS: A Concept-based Hierarchical Text Segmentation approach