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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2410.21315 |
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| _version_ | 1866917819548958720 |
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| author | Bugueño, Margarita Hamdan, Hazem Abou de Melo, Gerard |
| author_facet | Bugueño, Margarita Hamdan, Hazem Abou de Melo, Gerard |
| contents | Heterogeneous graph neural networks have recently gained attention for long document summarization, modeling the extraction as a node classification task. Although effective, these models often require external tools or additional machine learning models to define graph components, producing highly complex and less intuitive structures. We present GraphLSS, a heterogeneous graph construction for long document extractive summarization, incorporating Lexical, Structural, and Semantic features. It defines two levels of information (words and sentences) and four types of edges (sentence semantic similarity, sentence occurrence order, word in sentence, and word semantic similarity) without any need for auxiliary learning models. Experiments on two benchmark datasets show that GraphLSS is competitive with top-performing graph-based methods, outperforming recent non-graph models. We release our code on GitHub. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_21315 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization Bugueño, Margarita Hamdan, Hazem Abou de Melo, Gerard Computation and Language Artificial Intelligence Heterogeneous graph neural networks have recently gained attention for long document summarization, modeling the extraction as a node classification task. Although effective, these models often require external tools or additional machine learning models to define graph components, producing highly complex and less intuitive structures. We present GraphLSS, a heterogeneous graph construction for long document extractive summarization, incorporating Lexical, Structural, and Semantic features. It defines two levels of information (words and sentences) and four types of edges (sentence semantic similarity, sentence occurrence order, word in sentence, and word semantic similarity) without any need for auxiliary learning models. Experiments on two benchmark datasets show that GraphLSS is competitive with top-performing graph-based methods, outperforming recent non-graph models. We release our code on GitHub. |
| title | GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.21315 |