Automatically generating or captioning music playlist titles given a set of tracks is of significant interest in music streaming services as customized playlists are widely used in personalized music recommendation, and well-composed text titles attract users and help their music discovery. We present an encoder-decoder model that generates a playlist title from a sequence of music tracks. While previous work takes track IDs as tokenized input for playlist title generation, we use artist IDs corresponding to the tracks to mitigate the issue from the long-tail distribution of tracks included in the playlist dataset. Also, we introduce a chronological data split method to deal with newly-released tracks in real-world scenarios. Comparing the track IDs and artist IDs as input sequences, we show that the artist-based approach significantly enhances the performance in terms of word overlap, semantic relevance, and diversity.

翻译：在一组音轨中自动生成或字幕播放列表标题对音乐流服务非常感兴趣,因为在个性化音乐建议中广泛使用定制的播放列表,而精心组合的文本标题吸引用户并帮助他们的音乐发现。我们展示了一个编码器解码器模型,该模型从音轨序列中生成播放列表标题。虽然先前的工作将音轨标识作为播放列表标题生成的象征性输入,但我们使用与音轨相对应的艺人标识,从播放列表数据集中包含的音轨的长尾分布中缓解问题。此外,我们采用了按时间顺序排列的数据分割法来处理现实世界情景中新释放的音轨。将音轨ID和艺术家ID作为输入序列进行比较,我们展示了以艺术家为基础的方法在文字重叠、语义相关性和多样性方面大大提升了性能。