Music Playlist Title Generation: A Machine-Translation Approach

Juhan Nam; Junwon Lee; Seungheon Doh

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Music Playlist Title Generation: A Machine-Translation Approach

SeungHeon Doh, Junwon Lee, Juhan Nam

Machine Learning · Submitted 3 Oct 2021

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The result shows that the Transformer models generally outperform the RNN model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,424 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.13 · human participants 0.04 · controlled comparison 0.68 · randomized assignment 0.02 · states quantitative result 0.05 · compares with prior results 0.79 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

We propose a machine-translation approach to automatically generate a playlist title from a set of music tracks. We take a sequence of track IDs as input and a sequence of words in a playlist title as output, adapting the sequence-to-sequence framework based on Recurrent Neural Network (RNN) and Transformer to the music data. Considering the orderless nature of music tracks in a playlist, we propose two techniques that remove the order of the input sequence. One is data augmentation by shuffling and the other is deleting the positional encoding. We also reorganize the existing music playlist datasets to generate phrase-level playlist titles. The result shows that the Transformer models generally outperform the RNN model. Also, removing the order of input sequence improves the performance further.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	We propose a machine-translation approach to automatically generate a playlist title from a set of music tracks.	contribution 1.00	0.81	0.05	asserted 0.90
s1	We take a sequence of track IDs as input and a sequence of words in a playlist title as output, adapting the sequence-to-sequence framework based on Recurrent Neural Network (RNN) and Transformer to the music data.	method 0.99	0.91	0.04	asserted 0.97
s2	Considering the orderless nature of music tracks in a playlist, we propose two techniques that remove the order of the input sequence.	contribution 0.55	0.90	0.04	asserted 0.97
s3	One is data augmentation by shuffling and the other is deleting the positional encoding.	method 0.92	0.92	0.03	asserted 0.98
s4	We also reorganize the existing music playlist datasets to generate phrase-level playlist titles.	method 0.80	0.93	0.03	asserted 0.96
s5	The result shows that the Transformer models generally outperform the RNN model.	result 1.00	0.93	0.36	asserted 0.99
s6	Also, removing the order of input sequence improves the performance further.	result 1.00	0.93	0.29	asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.86.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

We propose a machine-translation approach to automatically generate a playlist title from a set of music tracks. We take a sequence of track IDs as input and a sequence of words in a playlist title as output, adapting the sequence-to-sequence framework based on Recurrent Neural Network (RNN) and Transformer to the music data. Considering the orderless nature of music tracks in a playlist, we propose two techniques that remove the order of the input sequence. One is data augmentation by shuffling and the other is deleting the positional encoding. We also reorganize the existing music playlist datasets to generate phrase-level playlist titles. The result shows that the Transformer models generally outperform the RNN model. Also, removing the order of input sequence improves the performance further.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it

Cites it as background 1 paper

Traditional music understanding models are developed for specific purposes and with dedicated model design, including single-track music captioning [ref] , playlist title generation [ref] and conversational music recommendation [ref] .

Futga: Towards Fine-grained Music Understanding through Temporally-enhanced Generative Augmentation · Introduction · 0.89

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