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Eliciting and Analyzing Male Mouse Ultrasonic Vocalization USV Songs
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Learning to Generate Tips from Song Reviews.

Jingya Zang1, Cuiyun Gao1, Yupan Chen1

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 1, 2023
PubMed
Summary

This study introduces GenTMS, a new framework for generating concise song tips from reviews. GenTMS helps users make faster, informed music choices by extracting key insights.

Keywords:
Data annotationPre-trained modelReview rankingSong reviewsTip generation

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Area of Science:

  • Natural Language Processing
  • Music Information Retrieval

Background:

  • Song reviews are vital for online music platforms, aiding user decision-making.
  • Existing song reviews are often lengthy and lack concise, actionable insights.
  • Efficiently extracting meaningful information from song reviews is a significant challenge.

Purpose of the Study:

  • To address the challenge of extracting concise insights from song reviews.
  • To propose an automated framework for generating song tips from reviews.
  • To create a novel dataset for the song tip generation task in the music domain.

Main Methods:

  • Developed the MTips dataset with 8,003 Chinese tips/non-tips across 128 songs and five genres.
  • Proposed the GenTMS (Generative Tip generation for Music Services) framework.
  • Utilized a learning-to-generate approach for tip extraction.

Main Results:

  • GenTMS achieved a top-10 precision of 85.56% on the MTips dataset, outperforming baselines by at least 3.34%.
  • The framework demonstrated strong performance on previously unseen songs, with an average top-10 precision of 78.89%.
  • Experimental results validate the effectiveness of GenTMS in generating informative song tips.

Conclusions:

  • The GenTMS framework is effective for automatically generating concise and insightful song tips from reviews.
  • This approach enhances user experience on music platforms by facilitating quicker, more informed decisions.
  • The developed MTips dataset provides a valuable resource for future research in music information retrieval and NLP.