从歌曲评论中生成提示：一个新的数据集和框架
Generating Tips from Song Reviews: A New Dataset and Framework
摘要：
歌曲评论在在线音乐服务平台中发挥着重要作用。之前的研究表明，当用户看到有意义的歌曲评论时，他们可以做出更快，更明智的决定。然而，对音乐歌曲的评论一般都很长，并且大多数评论对用户来说是没有信息的。用户很难有效地掌握有意义的信息来做出决策。为了解决这个问题，一个实用的策略是提供提示，即，关于歌曲的简短、简洁、有同情心和自成一体的描述。提示是从歌曲评论中产生的，应该表达对歌曲的重要见解。据我们所知，没有以前的研究探讨了提示生成任务在音乐领域。在本文中，我们创建了一个数据集名为MTips的任务，并提出了一个框架名为GENTMS自动生成提示的歌曲评论。该数据集涉及来自128首歌曲的8，003个中文提示/非提示，这些歌曲分布在五种不同的歌曲类型中。实验结果表明，GENTMS达到前10名的精度为85.56%，超过基线模型至少3.34%。此外，为了模拟我们提出的框架的实际使用，我们还对以前未见过的歌曲进行了实验，在此期间，GENTMS也以平均78.89%的前10名精度达到了最佳性能。实验结果表明了该框架在音乐领域的技巧生成中的有效性。
英文摘要：
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.
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。
