[论文解读] EmbedRank: Unsupervised Keyphrase Extraction using Sentence Embeddings.

Kamil Bennani-Smires, Claudiu Musat+3 more|arXiv (Cornell University)|Jan 13, 2018

Advanced Text Analysis Techniques参考文献 20被引用 20

一句话总结

该论文提出 EmbedRank，一种无监督关键词提取方法，利用句子嵌入识别其语义表示最接近整体文档嵌入的短语，其 F 得分高于当前最先进的基于图的方法，同时显著更快且更简单。该方法进一步通过基于嵌入的 MMR 方法增强多样性，尽管 F 得分未提升，但用户更偏好该方法。

ABSTRACT

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as human-facing use cases. Existing supervised systems for keyphrase extraction require large amounts of labeled training data and generalize very poorly outside the domain of the training data. At the same time, unsupervised systems found in the literature have poor accuracy, and often do not generalize well, as they require the input document to belong to a larger corpus also given as input. Furthermore, both supervised and unsupervised methods are often too slow for real-time scenarios and suffer from over-generation. Addressing these drawbacks, in this paper, we introduce an unsupervised method for keyphrase extraction from single documents that leverages sentence embeddings. By selecting phrases whose semantic embeddings are close to the embeddings of the whole document, we are able to separate the best candidate phrases from the rest. We show that our embedding-based method is not only simpler, but also more effective than graph-based state of the art systems, achieving higher F-scores on standard datasets. Simplicity is a significant advantage, especially when processing large amounts of documents from the Web, resulting in considerable speed gains. Moreover, we describe how to increase coverage and diversity among the selected keyphrases by introducing an embedding-based maximal marginal relevance (MMR) for new phrases. A user study including over 200 votes showed that, although reducing the phrase semantic overlap leads to no gains in terms of F-score, our diversity enriched selection is preferred by humans.

研究动机与目标

• 解决监督式关键词系统存在的局限性，即需要大规模标注数据集且在不同领域间泛化能力差。
• 克服现有无监督方法依赖大规模外部语料库所导致的泛化能力差和计算成本高的问题。
• 开发一种更快、更简单且更有效的无监督关键词提取方法，适用于大规模网络文档处理。
• 在不牺牲提取准确率的前提下，提升关键词的覆盖度和多样性。

提出的方法

• 该方法将输入文档中所有句子嵌入的平均值计算为文档级别的句子嵌入。
• 使用预训练的句子编码器对候选关键词进行嵌入，并使用余弦相似度计算其与文档嵌入的语义相似度。
• 根据与文档嵌入的相似度对短语进行排序，选择排名靠前的短语作为关键词，构成无监督提取机制的核心。
• 应用基于嵌入的最大边际相关性（MMR）策略对已选短语进行重排序，以减少语义冗余并提升多样性。
• MMR 目标函数在评分中同时考虑相关性（与文档嵌入的相似度）和多样性（与已选短语的最小相似度）。
• 该方法设计高效且可扩展，支持大规模网络文档的实时处理。

实验结果

研究问题

• RQ1 基于句子嵌入的无监督关键词提取方法是否能在 F 得分和效率方面超越现有的基于图的最先进系统？
• RQ2 当该方法应用于未见过的领域文档时，其性能如何，且无需微调或访问外部语料库？
• RQ3 通过基于嵌入的 MMR 方法引入多样性，在不降低 F 得分等标准评估指标的前提下，能在多大程度上提升用户偏好？
• RQ4 该方法是否能在不依赖标注训练数据或大型参考语料库的情况下实现高质量的关键词提取？

主要发现

• EmbedRank 在标准基准数据集上的 F 得分高于当前最先进的基于图的无监督关键词提取方法。
• 该方法显著快于现有方法且实现更简单，适用于实时和大规模文档处理。
• 尽管通过基于嵌入的 MMR 实现的多样性增强并未提升 F 得分，但在超过 200 人次的人工评估中，用户更偏好该方法。
• 该方法在无需标注数据或访问外部语料库的情况下，在不同领域间表现出良好的泛化能力，展现出强大的零样本性能。