Bidirectional LSTM Recurrent Neural Network for Keyphrase Extraction

Our paper “Bidirectional LSTM Recurrent Neural Networkfor Keyphrase Extraction by M. Basaldella, E. Antolli, G. Serra and C. Tasso has been accepted for publication by the Italian Research Conference on Digital Libraries 2018.

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.