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.
CITATION STYLE
Basaldella, M., Antolli, E., Serra, G., & Tasso, C. (2018). Bidirectional LSTM recurrent neural network for keyphrase extraction. In Communications in Computer and Information Science (Vol. 806, pp. 180–187). Springer Verlag. https://doi.org/10.1007/978-3-319-73165-0_18
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