关键词: 文本摘要, 深度学习, 循环神经网络, 覆盖率机制

Abstract: In recent years, text information has experienced explosive growth, and people haven??t enough time to read all these texts. Therefore, how to automatically extract key information from massive texts is particularly important. Text summarization technology can solve this problem. At present, sequence to sequence model with attention mechanism is usually used to generate text summary. However, the attention mechanism is independent at  each moment, and the text information generated at the previous moment is not taken into account. This results in the term repetition in the summary. In order to solve this problem, this paper proposes new coverage models. In these models, coverage vector is developed to record the historical attention weight distribution and adjust the current attention weight distribution, making the summarization model pay more attention to information that is not used. The experiment conducted on the Sina Weibo data set shows that the proposed model with coverage mechanism performs better than the normal sequence to sequence model.

Key words: text summarization, deep learning, recurrent neural network, coverage mechanism