  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.
