论文标题
使用稀疏编码的无监督提取意见摘要
Unsupervised Extractive Opinion Summarization Using Sparse Coding
论文作者
论文摘要
意见摘要是自动生成摘要的任务，这些摘要封装了来自多个用户评论的信息。我们介绍语义自动编码器（SEMAE），以无监督的方式执行提取意见摘要。 Semae使用字典学习来隐式从评论中捕获语义信息，并通过语义单元学习每个句子的潜在表示。语义单元应该捕获一个抽象的语义概念。我们的提取性摘要算法利用代表来确定数百个评论之间的代表性意见。 Semae还能够执行可控的摘要以生成特定方面的摘要。我们报告了太空和亚马逊数据集的强大性能，并执行实验以研究模型的功能。我们的代码可在https://github.com/brcsomnath/semae上公开获取。
Opinion summarization is the task of automatically generating summaries that encapsulate information from multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic information from the review and learns a latent representation of each sentence over semantic units. A semantic unit is supposed to capture an abstract semantic concept. Our extractive summarization algorithm leverages the representations to identify representative opinions among hundreds of reviews. SemAE is also able to perform controllable summarization to generate aspect-specific summaries. We report strong performance on SPACE and AMAZON datasets, and perform experiments to investigate the functioning of our model. Our code is publicly available at https://github.com/brcsomnath/SemAE.
