Transactions of Nanjing University of Aeronautics & Astronautics
A Hybrid Method of Extractive Text Summarization Based on Deep Learning and Graph Ranking Algorithms
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Abstract:
In the era of Big Data， we are faced with an inevitable and challenging problem of “overload information ”. To alleviate this problem， it is important to use effective automatic text summarization techniques to obtain the key information quickly and efficiently from the huge amount of text. In this paper， we propose a hybrid method of extractive text summarization based on deep learning and graph ranking algorithms （ETSDG）. In this method， a pre-trained deep learning model is designed to yield useful sentence embeddings. Given the association between sentences in raw documents， a traditional LexRank algorithm with fine-tuning is adopted fin ETSDG. In order to improve the performance of the extractive text summarization method， we further integrate the traditional LexRank algorithm with deep learning. Testing results on the data set DUC2004 show that ETSDG has better performance in ROUGE metrics compared with certain benchmark methods.
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TP 391
CHINA AERO-SCIENCES
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