Abstract
To achieve state-of-the-art performance, keyphrase extraction systems rely on domain-specific knowledge and sophisticated features. In this paper, we propose a neural network architecture based on a Bidirectional Long Short-Term Memory Recurrent Neural Network that is able to detect the main topics on the input documents without the need of defining new hand-crafted features. A preliminary experimental evaluation on the well-known INSPEC dataset confirms the effectiveness of the proposed solution.
M. Basaldella and E. Antolli—Equally Contributed.
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Basaldella, M., Antolli, E., Serra, G., Tasso, C. (2018). Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction. In: Serra, G., Tasso, C. (eds) Digital Libraries and Multimedia Archives. IRCDL 2018. Communications in Computer and Information Science, vol 806. Springer, Cham. https://doi.org/10.1007/978-3-319-73165-0_18
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