Reviews of songs play an important role in online music service platforms. Prior research shows that users can make quicker and more informed decisions when presented with meaningful song reviews. However, reviews of music songs are generally long in length and most of them are non-informative for users. It is difficult for users to efficiently grasp meaningful messages for making decisions. To solve this problem, one practical strategy is to provide tips, i.e., short, concise, empathetic, and self-contained descriptions about songs. Tips are produced from song reviews and should express non-trivial insights about the songs. To the best of our knowledge, no prior studies have explored the tip generation task in music domain. In this paper, we create a dataset named MTips for the task and propose a framework named GENTMS for automatically generating tips from song reviews. The dataset involves 8,003 Chinese tips/non-tips from 128 songs which are distributed in five different song genres. Experimental results show that GENTMS achieves top-10 precision at 85.56%, outperforming the baseline models by at least 3.34%. Besides, to simulate the practical usage of our proposed framework, we also experiment with previously-unseen songs, during which GENTMS also achieves the best performance with top-10 precision at 78.89% on average. The results demonstrate the effectiveness of the proposed framework in tip generation of the music domain.
翻译:对歌曲的审查在网上音乐服务平台中起着重要作用。 先前的研究显示,用户在展示有意义的歌曲评论时可以做出更快捷、更知情的决定。 但是,对音乐歌曲的审查一般时间很长,而且大部分对用户来说都不是信息规范。 用户很难有效地掌握有意义的信息来做出决策。 要解决这个问题, 一项实用的战略是提供小费, 即短、 简、 同情、 和自成品的歌曲描述。 提示来自歌曲评论, 并且应该表达对歌曲的非三重洞察力。 根据我们的知识, 先前的研究没有探讨过音乐领域的初创任务。 在本文中, 我们为任务创建了一个名为 MTips 的数据集, 并提出了一个名为 GENTMS 的框架, 以自动生成歌曲审查的提示。 数据集包括以五种不同的歌曲形式散发的128种歌曲中的8 003个中小费/非小费。 实验结果显示, GentMS 在85.56%的首创模型中,我们至少完成了3.34%的成绩。 此外, 将G- 10 10 格式框架的顶级的实验也模拟了我们之前的实地应用。