Song Lyrics Summarization Inspired by Audio Thumbnailing

Michael Fell 1 Elena Cabrio 1 Fabien Gandon 1 Alain Giboin 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : Given the peculiar structure of songs, applying generic text summarization methods to lyrics can lead to the generation of highly redundant and incoherent text. In this paper, we propose to enhance state-of-the-art text summarization approaches with a method inspired by audio thumbnailing. Instead of searching for the thumbnail clues in the audio of the song, we identify equivalent clues in the lyrics. We then show how these summaries that take into account the audio nature of the lyrics outperform the generic methods according to both an automatic evaluation and human judgments.
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Submitted on : Sunday, September 8, 2019 - 1:15:57 PM
Last modification on : Tuesday, September 10, 2019 - 1:18:35 AM


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  • HAL Id : hal-02281138, version 1



Michael Fell, Elena Cabrio, Fabien Gandon, Alain Giboin. Song Lyrics Summarization Inspired by Audio Thumbnailing. Conference on Recent Advances in Natural Language Processing (RANLP), Sep 2019, Varna, Bulgaria. ⟨hal-02281138⟩



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