用状态空间模型提升低资源场景下的摘要提取效果
State Space Models for Extractive Summarization in Low Resource Scenarios
- 结合Mamba与庞加莱压缩，捕捉句子语义特征
- 在亚马逊评论数据集上ROUGE分数优于现有方法
- 适合资源有限但需高效摘要的场景
抽取式摘要旨在从文本中选取最相关句子。近期研究聚焦于低资源环境下性能提升。受此启发，本文提出MPoincareSum方法：利用Mamba状态空间模型生成评论与句子语义表示，并进行拼接；采用庞加莱压缩筛选最具意义的特征；通过线性层预测句子相关性；最后对相关句进行改写生成最终摘要。为验证有效性，我们在Amazon评论数据集上进行了大量实验，使用ROUGE分数评估性能。结果表明，MPoincareSum在低资源场景下优于多个现有方法。
原文摘要 · Abstract (English)
Extractive summarization involves selecting the most relevant sentences from a text. Recently, researchers have focused on advancing methods to improve state-of-the-art results in low-resource settings. Motivated by these advancements, we propose the MPoincareSum method. This method applies the Mamba state space model to generate the semantics of reviews and sentences, which are then concatenated. A Poincare compression is used to select the most meaningful features, followed by the application of a linear layer to predict sentence relevance based on the corresponding review. Finally, we paraphrase the relevant sentences to create the final summary. To evaluate the effectiveness of MPoincareSum, we conducted extensive experiments using the Amazon review dataset. The performance of the method was assessed using ROUGE scores. The experimental results demonstrate that MPoincareSum outperforms several existing approaches in the literature
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。