BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation (2505.16965v2)

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.

• The paper introduces BP-Seg, which employs belief propagation within a graphical model to segment text non-contiguously.
• It builds sentence embeddings and constructs a semantic graph that groups semantically related sentences, outperforming methods like GraphSeg and k-means.
• Experimental results on datasets such as Choi show superior ARI and NMI scores, demonstrating the model’s robustness and effectiveness.

BP-Seg: A Graphical Model Approach to Unsupervised and Non-Contiguous Text Segmentation Using Belief Propagation

Introduction

The paper "BP-Seg: A graphical model approach to unsupervised and non-contiguous text segmentation using belief propagation" presents an innovative method for segmenting text into semantically coherent segments using a graphical model-based unsupervised learning approach. The proposed method, BP-Seg, distinguishes itself by considering both local coherence and the global semantic similarity of sentences, even when they are not contiguous. This model leverages belief propagation to partition text effectively, addressing limitations in traditional segmentation techniques that primarily focus on contiguous segmentation.

Methodology

The BP-Seg algorithm is built around three key stages: sentence embedding, graphical model construction, and the application of belief propagation (BP).

1. Sentence Embeddings: Sentences are transformed into vector representations using embedding techniques such as those provided by the sentence-transformers library. These embeddings map semantically similar sentences close to each other in the vector space, facilitating the construction of a semantic graph.
2. Graphical Model Construction: A graph is built where nodes represent sentence embeddings, and edges encode semantic similarity. The segmentation task is framed as finding the most probable assignment of sentences to segments, modeled as a factor graph with unary and pairwise potentials.
3. Belief Propagation: The BP algorithm iteratively updates messages between nodes to compute the marginal probabilities of segment assignments. The method applies the sum-product variant of BP, allowing for efficient inference that considers both semantic similarity and sentence adjacency.

The algorithm, described in detail in the pseudocode, allows non-contiguous sentences to be grouped based on semantic relationships, making it suitable for various applications including prompt pruning in LLMs and document summarization.

Experimental Evaluation

Experiments demonstrate the efficacy of BP-Seg on both synthetic and benchmark datasets.

• Illustrative Example: BP-Seg was tested on a short, mixed-content document generated by GPT-4 to evaluate its ability to segment text into meaningful clusters. The method successfully grouped semantically related sentences, such as those linked to tennis, outperforming other methods like GraphSeg and $k$-means under most configurations.
• Choi Dataset: On the standard Choi dataset, BP-Seg demonstrated superior performance in terms of Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI), metrics that are better suited for evaluating non-contiguous segmentation. BP-Seg consistently outperformed baseline methods, highlighting its robustness and advantage in capturing semantic coherence.

Related Work

Previous work, such as GraphSeg, primarily focused on contiguous segmentation, often requiring additional linguistic resources. In contrast, BP-Seg relies on semantic embeddings and a probabilistic inference strategy, offering a distinct advantage in flexibility and domain independence. Methods like $k$-means lack the contextual sensitivity achieved by BP-Seg due to their isolation of sentence embeddings as individual data points.

Conclusion

BP-Seg introduces a novel approach to text segmentation that balances local coherence with the flexibility of grouping non-contiguous sentences based on semantic similarity. The method's application of belief propagation within a graphical model framework marks a significant contribution, particularly in tasks demanding high-level semantic organization. Future work could explore its application in prompt optimization for LLMs, information retrieval, and summarization, areas where non-contiguous semantic coherence can enhance performance.

In summary, BP-Seg sets a precedent for addressing non-contiguous segmentation challenges, providing a robust tool for semantic text analysis with applications across a broad spectrum of natural language processing tasks.