Global-SEG: Text Semantic Segmentation Based on Global Semantic Pair Relations

Text semantic segmentation is a crucial task in language understanding, as subsequent natural language processing tasks often require cohesive semantic blocks. This paper introduces a new perspective on this task by utilizing global semantic pair relations from both token- and sentence-level language models. This approach addresses the limitations of prior work, which concentrated solely on individual semantic units like sentences. Our model processes both local and global levels of sentence semantics via encoders and then combines the semantics obtained at each stage into a semantic embedding matrix. This matrix is then fed through a convolutional neural network and finally used as input through another encoder. This process enables the identification of semantic segmentation boundaries by describing the relationships of global semantic pairs. Furthermore, we utilize semantic embeddings from large language models and consider the positional information of text within the document to assess their efficacy in augmenting semantics. We test our model with both contemporary and historical corpora, and the results demonstrate that our approach outperforms benchmarks on each dataset.