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Existing keyphrase extraction approaches often suffer from issues such as the sparsity and brevity of short text (e.g., headlines, queries, and tweets). In this paper, we propose a novel keyphrase extraction method for short text by utilizing recurrent neural networks. The main idea behind our approach is to classify short text into a relevant class or category and extract keyphrases from important words in the class or category. Unlike previous supervised approaches that need the information of annotated keyphrases, our approach requires only a text classification dataset (i.e., DBpedia), which is easier to use and requires less human effort. In our approach, we first feed short text into the attention-based neural network for text classification. We then compute attention weights of each word in input short text. Subsequently, we detect keyphrase candidates by chunking phrases and summing the attention weights of compositional words in the chunked phrase. The experimental results clearly show the efficacy of our approach on real-world datasets, such as headlines, queries, and tweets. The proposed method outperforms the Microsoft Cognitive Services and IBM Watson Natural Language Understanding service for keyphrase extraction in terms of F1-score and acceptable percentage on the NYT and Question datasets. Further, we confirm that the proposed method is comparable to supervised methods for keyphrase extraction from short text in the Tweet dataset.

1 Introduction 1
2 Preliminary 4
 2.1 Attention-based LSTM for Text Classification 4
 2.2 Fine-grained Topic Knowledge 5
3 Methodology 8
 3.1 Keyphrase Extraction from Topic Classification 11
  3.1.1 Attention-based Topic Classification 11
  3.1.2 Keyphrase Selection 12
 3.2 Incorporating Fine-grained Topic Knowledge into Topic Classification 13
  3.2.1 Fine-grained Topic Embedding 13
  3.2.2 Attention Mechanism with Fine-grained Topic 14
4 Experiments 17
 4.1 Datasets 17
  4.1.1 Topic Classification Training Dataset 17
  4.1.2 Keyphrase Extraction Evaluation Dataset 17
 4.2 Experiment Settings 18
  4.2.1 Baselines 18
  4.2.2 Model Parameters 19
 4.3 Keyphrase Extraction Results 19
  4.3.1 Quantitative Evaluation 19
  4.3.2 Qualitative Evaluation 21
  4.3.3 Application to Tweet 24
 4.4 Analysis 25
  4.4.1 Visualization of Attention 25
  4.4.2 Analysis of Topic Classification Results 27
5 Related Work 29
 5.1 Supervised Approach to Keyphrase Extraction 29
 5.2 Graph-based Ranking Approach to Keyphrase Extraction 30
 5.3 Topic-based Clustering Method for Keyphrase Extraction 30
 5.4 Combining Knowledge with Neural Network 30
6 Conclusion 32
 6.1 Conclusion 32
 6.2 Future Work 32
Bibliography 34