BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation

• URL: http://arxiv.org/abs/2505.16965v1
• Date: Thu, 22 May 2025 17:46:23 GMT
• Title: BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation
• Authors: Fengyi Li, Kayhan Behdin, Natesh Pillai, Xiaofeng Wang, Zhipeng Wang, Ercan Yildiz,
• Abstract summary: We propose a graphical model-based unsupervised learning approach, named BP-Seg for efficient text segmentation.<n>Our method not only considers local coherence, capturing the intuition that adjacent sentences are often more related, but also effectively groups sentences that are distant in the text yet semantically similar.
• Score: 5.9737438702986765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text segmentation based on the semantic meaning of sentences is a fundamental task with broad utility in many downstream applications. In this paper, we propose a graphical model-based unsupervised learning approach, named BP-Seg for efficient text segmentation. Our method not only considers local coherence, capturing the intuition that adjacent sentences are often more related, but also effectively groups sentences that are distant in the text yet semantically similar. This is achieved through belief propagation on the carefully constructed graphical models. Experimental results on both an illustrative example and a dataset with long-form documents demonstrate that our method performs favorably compared to competing approaches.

Related papers

• Token-Level Graphs for Short Text Classification [1.6819960041696331]
  We propose an approach which constructs text graphs entirely based on tokens obtained through pre-trained language models (PLMs)<n>Our method captures contextual and semantic information, overcomes vocabulary constraints, and allows for context-dependent word meanings.<n> Experimental results demonstrate how our method consistently achieves higher scores or on-par performance with existing methods.
  arXiv  Detail & Related papers  (2024-12-17T10:19:44Z)
• Diffusion Model is Secretly a Training-free Open Vocabulary Semantic Segmenter [47.29967666846132]
  generative text-to-image diffusion models are highly efficient open-vocabulary semantic segmenters. We introduce a novel training-free approach named DiffSegmenter to generate realistic objects that are semantically faithful to the input text. Extensive experiments on three benchmark datasets show that the proposed DiffSegmenter achieves impressive results for open-vocabulary semantic segmentation.
  arXiv  Detail & Related papers  (2023-09-06T06:31:08Z)
• A Comparative Study of Sentence Embedding Models for Assessing Semantic Variation [0.0]
  We compare several recent sentence embedding methods via time-series of semantic similarity between successive sentences and matrices of pairwise sentence similarity for multiple books of literature. We find that most of the sentence embedding methods considered do infer highly correlated patterns of semantic similarity in a given document, but show interesting differences.
  arXiv  Detail & Related papers  (2023-08-08T23:31:10Z)
• Relational Sentence Embedding for Flexible Semantic Matching [86.21393054423355]
  We present Sentence Embedding (RSE), a new paradigm to discover further the potential of sentence embeddings. RSE is effective and flexible in modeling sentence relations and outperforms a series of state-of-the-art embedding methods.
  arXiv  Detail & Related papers  (2022-12-17T05:25:17Z)
• Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document [5.9630342951482085]
  We propose an unsupervised extractive summarizaiton method with heterogeneous graph embeddings (HGEs) for Chinese document. Experimental results demonstrate that our method consistently outperforms the strong baseline in three summarization datasets.
  arXiv  Detail & Related papers  (2022-11-09T06:07:31Z)
• StrokeNet: Stroke Assisted and Hierarchical Graph Reasoning Networks [31.76016966100244]
  StrokeNet is proposed to effectively detect the texts by capturing the fine-grained strokes. Different from existing approaches that represent the text area by a series of points or rectangular boxes, we directly localize strokes of each text instance.
  arXiv  Detail & Related papers  (2021-11-23T08:26:42Z)
• Hierarchical Heterogeneous Graph Representation Learning for Short Text Classification [60.233529926965836]
  We propose a new method called SHINE, which is based on graph neural network (GNN) for short text classification. First, we model the short text dataset as a hierarchical heterogeneous graph consisting of word-level component graphs. Then, we dynamically learn a short document graph that facilitates effective label propagation among similar short texts.
  arXiv  Detail & Related papers  (2021-10-30T05:33:05Z)
• HETFORMER: Heterogeneous Transformer with Sparse Attention for Long-Text Extractive Summarization [57.798070356553936]
  HETFORMER is a Transformer-based pre-trained model with multi-granularity sparse attentions for extractive summarization. Experiments on both single- and multi-document summarization tasks show that HETFORMER achieves state-of-the-art performance in Rouge F1.
  arXiv  Detail & Related papers  (2021-10-12T22:42:31Z)
• Contextualized Semantic Distance between Highly Overlapped Texts [85.1541170468617]
  Overlapping frequently occurs in paired texts in natural language processing tasks like text editing and semantic similarity evaluation. This paper aims to address the issue with a mask-and-predict strategy. We take the words in the longest common sequence as neighboring words and use masked language modeling (MLM) to predict the distributions on their positions. Experiments on Semantic Textual Similarity show NDD to be more sensitive to various semantic differences, especially on highly overlapped paired texts.
  arXiv  Detail & Related papers  (2021-10-04T03:59:15Z)
• Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration [25.159601117722936]
  We propose a contrastive fine-tuning objective that enables BERT to produce more powerful phrase embeddings. Our approach relies on a dataset of diverse phrasal paraphrases, which is automatically generated using a paraphrase generation model. As a case study, we show that Phrase-BERT embeddings can be easily integrated with a simple autoencoder to build a phrase-based neural topic model.
  arXiv  Detail & Related papers  (2021-09-13T20:31:57Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Extractive Summarization as Text Matching [123.09816729675838]
  This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems. We formulate the extractive summarization task as a semantic text matching problem. We have driven the state-of-the-art extractive result on CNN/DailyMail to a new level (44.41 in ROUGE-1)
  arXiv  Detail & Related papers  (2020-04-19T08:27:57Z)

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.