CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation

• URL: http://arxiv.org/abs/2503.23671v2
• Date: Wed, 02 Apr 2025 07:47:56 GMT
• Title: CrossFormer: Cross-Segment Semantic Fusion for Document Segmentation
• Authors: Tongke Ni, Yang Fan, Junru Zhou, Xiangping Wu, Qingcai Chen,
• Abstract summary: Text semantic segmentation involves partitioning a document into multiple paragraphs with continuous semantics.<n>Traditional approaches have relied on preprocessing documents into segments to address input length constraints.<n>We present CrossFormer, a transformer-based model featuring a novel cross-segment fusion module.
• Score: 16.70112752541306
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Text semantic segmentation involves partitioning a document into multiple paragraphs with continuous semantics based on the subject matter, contextual information, and document structure. Traditional approaches have typically relied on preprocessing documents into segments to address input length constraints, resulting in the loss of critical semantic information across segments. To address this, we present CrossFormer, a transformer-based model featuring a novel cross-segment fusion module that dynamically models latent semantic dependencies across document segments, substantially elevating segmentation accuracy. Additionally, CrossFormer can replace rule-based chunk methods within the Retrieval-Augmented Generation (RAG) system, producing more semantically coherent chunks that enhance its efficacy. Comprehensive evaluations confirm CrossFormer's state-of-the-art performance on public text semantic segmentation datasets, alongside considerable gains on RAG benchmarks.

Related papers

• S2 Chunking: A Hybrid Framework for Document Segmentation Through Integrated Spatial and Semantic Analysis [0.0]
  Document chunking is a critical task in natural language processing (NLP)<n>This paper introduces a novel hybrid approach that combines layout structure, semantic analysis, and spatial relationships.<n> Experimental results demonstrate that this approach outperforms traditional methods.
  arXiv  Detail & Related papers  (2025-01-08T09:06:29Z)
• Hypergraph based Understanding for Document Semantic Entity Recognition [65.84258776834524]
  We build a novel hypergraph attention document semantic entity recognition framework, HGA, which uses hypergraph attention to focus on entity boundaries and entity categories at the same time. Our results on FUNSD, CORD, XFUNDIE show that our method can effectively improve the performance of semantic entity recognition tasks.
  arXiv  Detail & Related papers  (2024-07-09T14:35:49Z)
• From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions [63.11097464396147]
  We introduce a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. We also introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-27T15:59:37Z)
• Toward Unifying Text Segmentation and Long Document Summarization [31.084738269628748]
  We study the role that section segmentation plays in extractive summarization of written and spoken documents. Our approach learns robust sentence representations by performing summarization and segmentation simultaneously. Our findings suggest that the model can not only achieve state-of-the-art performance on publicly available benchmarks, but demonstrate better cross-genre transferability.
  arXiv  Detail & Related papers  (2022-10-28T22:07:10Z)
• Open-world Semantic Segmentation via Contrasting and Clustering Vision-Language Embedding [95.78002228538841]
  We propose a new open-world semantic segmentation pipeline that makes the first attempt to learn to segment semantic objects of various open-world categories without any efforts on dense annotations. Our method can directly segment objects of arbitrary categories, outperforming zero-shot segmentation methods that require data labeling on three benchmark datasets.
  arXiv  Detail & Related papers  (2022-07-18T09:20:04Z)
• Graph Adaptive Semantic Transfer for Cross-domain Sentiment Classification [68.06496970320595]
  Cross-domain sentiment classification (CDSC) aims to use the transferable semantics learned from the source domain to predict the sentiment of reviews in the unlabeled target domain. We present Graph Adaptive Semantic Transfer (GAST) model, an adaptive syntactic graph embedding method that is able to learn domain-invariant semantics from both word sequences and syntactic graphs.
  arXiv  Detail & Related papers  (2022-05-18T07:47:01Z)
• Topical Change Detection in Documents via Embeddings of Long Sequences [4.13878392637062]
  We formulate the task of text segmentation as an independent supervised prediction task. By fine-tuning on paragraphs of similar sections, we are able to show that learned features encode topic information. Unlike previous approaches, which mostly operate on sentence-level, we consistently use a broader context.
  arXiv  Detail & Related papers  (2020-12-07T12:09:37Z)
• Referring Image Segmentation via Cross-Modal Progressive Comprehension [94.70482302324704]
  Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities. We propose a Cross-Modal Progressive (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task.
  arXiv  Detail & Related papers  (2020-10-01T16:02:30Z)
• Improving Semantic Segmentation via Decoupled Body and Edge Supervision [89.57847958016981]
  Existing semantic segmentation approaches either aim to improve the object's inner consistency by modeling the global context, or refine objects detail along their boundaries by multi-scale feature fusion. In this paper, a new paradigm for semantic segmentation is proposed. Our insight is that appealing performance of semantic segmentation requires textitexplicitly modeling the object textitbody and textitedge, which correspond to the high and low frequency of the image. We show that the proposed framework with various baselines or backbone networks leads to better object inner consistency and object boundaries.
  arXiv  Detail & Related papers  (2020-07-20T12:11:22Z)
• Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation [9.416757363901295]
  We introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model -- a neural architecture consisting of two hierarchically connected Transformer networks -- is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones.
  arXiv  Detail & Related papers  (2020-01-03T17:06:41Z)

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.