GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization
<|reference_start|>GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization: Pre-trained language models are increasingly being used in multi-document summarization tasks. However, these models need large-scale corpora for pre-training and are domain-dependent. Other non-neural unsupervised summarization approaches mostly rely on key sentence extraction, which can lead to information loss. To address these challenges, we propose a lightweight yet effective unsupervised approach called GLIMMER: a Graph and LexIcal features based unsupervised Multi-docuMEnt summaRization approach. It first constructs a sentence graph from the source documents, then automatically identifies semantic clusters by mining low-level features from raw texts, thereby improving intra-cluster correlation and the fluency of generated sentences. Finally, it summarizes clusters into natural sentences. Experiments conducted on Multi-News, Multi-XScience and DUC-2004 demonstrate that our approach outperforms existing unsupervised approaches. Furthermore, it surpasses state-of-the-art pre-trained multi-document summarization models (e.g. PEGASUS and PRIMERA) under zero-shot settings in terms of ROUGE scores. Additionally, human evaluations indicate that summaries generated by GLIMMER achieve high readability and informativeness scores. Our code is available at https://github.com/Oswald1997/GLIMMER.<|reference_end|>
@article{liu2024glimmer:,
    title={GLIMMER: Incorporating Graph and Lexical Features in Unsupervised
  Multi-Document Summarization},
    author={Ran Liu, Ming Liu, Min Yu, Jianguo Jiang, Gang Li, Dan Zhang, Jingyuan
  Li, Xiang Meng, Weiqing Huang},
    journal={arXiv preprint arXiv:2408.10115},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10115},
    primaryClass={cs.CL}
}