自动语音摘要研究综述
Automatic Speech Summarisation: A Scoping Review
摘要：
语音摘要技术将人类语音作为输入，然后输出作为文本或语音的删节版本。语音摘要在从信息技术到医疗保健的许多领域都有应用，例如改善语音档案或减少临床文档负担。这一范围审查映射的演讲摘要文献，没有时间框架的限制，语言总结，研究方法，或论文类型。我们共审查了110篇论文的一组153通过文献检索和提取语音特征，方法，范围和训练语料库。大多数研究采用四种语音摘要架构之一：（1）句子提取和压缩;（2）特征提取和分类或基于排名的句子选择;（3）句子压缩和压缩摘要;以及（4）语言建模。我们还讨论了这些不同的方法和语音特征的优点和缺点。总体而言，监督方法（例如隐马尔可夫支持向量机，排名支持向量机，条件随机场）比无监督方法表现得更好。由于监督方法需要手动注释的训练数据，这可能是昂贵的，因此对无监督方法更感兴趣。最近对无监督方法的研究集中在扩展语言建模上，例如通过将Unigram建模与深度神经网络相结合。方案注册：在https://osf.io上注册了本范围审查的方案。
英文摘要：
Speech summarisation techniques take human speech as input and then output an abridged version as text or speech. Speech summarisation has applications in many domains from information technology to health care, for example improving speech archives or reducing clinical documentation burden. This scoping review maps the speech summarisation literature, with no restrictions on time frame, language summarised, research method, or paper type. We reviewed a total of 110 papers out of a set of 153 found through a literature search and extracted speech features used, methods, scope, and training corpora. Most studies employ one of four speech summarisation architectures: (1) Sentence extraction and compaction; (2) Feature extraction and classification or rank-based sentence selection; (3) Sentence compression and compression summarisation; and (4) Language modelling. We also discuss the strengths and weaknesses of these different methods and speech features. Overall, supervised methods (e.g. Hidden Markov support vector machines, Ranking support vector machines, Conditional random fields) performed better than unsupervised methods. As supervised methods require manually annotated training data which can be costly, there was more interest in unsupervised methods. Recent research into unsupervised methods focusses on extending language modelling, for example by combining Uni-gram modelling with deep neural networks. Protocol registration: The protocol for this scoping review is registered at https://osf.io.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
