基于语义感知的中文短文本摘要生成模型

Chinese Short Text Summarization Generation Model Based on Semantic-aware

文本摘要生成技术能够从海量数据中概括出关键信息,有效解决用户信息过载的问题。目前序列到序列模型被广泛应用于英文文本摘要生成领域,而在中文文本摘要生成领域没有对该模型进行深入研究。对于传统的序列到序列模型,解码器通过注意力机制将编码器输出的每一个词的隐藏状态作为原始文本完整的语义信息来生成摘要,但是编码器输出的每一个词的隐藏状态仅包含前、后词的语义信息,不包含原始文本完整的语义信息,导致生成摘要缺失原始文本的核心信息,影响生成摘要的准确性和可读性。为此,文中提出基于语义感知的中文短文本摘要生成模型SA-Seq2Seq,以结合注意力机制的序列到序列模型为基础,通过使用预训练模型BERT,在编码器中将中文短文本作为整体语义信息引入,使得每一个词包含整体语义信息;在解码器中将参考摘要作为目标语义信息计算语义不一致损失,以确保生成摘要的语义完整性。采用中文短文本摘要数据集LCSTS进行实验,结果表明,模型SA-Seq2Seq在评估标准ROUGE上的效果相对于基准模型有显著提高,其ROUGE-1,ROUGE-2和ROUGE-L评分在基于字符处理的数据集上分别提升了3.4%,7.1%和6.1%,在基于词语处理的数据集上分别提升了2.7%,5.4%和11.7%,即模型SA-Seq2Seq能够更有效地融合中文短文本的整体语义信息,挖掘其关键信息,确保生成摘要的流畅性和连贯性,可以应用于中文短文本摘要生成任务。

ion.In the conventional sequence-to-sequence model,the decoder applies the hidden state of each word output by the encoder as the overall semantic information through the attention mechanism,nevertheless the hidden state of each word which encoder outputs only in consideration of the front and back words of current word,which results in the generated summary missing the core information of the source text.To solve this problem,a semantic-aware based Chinese short text summarization generation model called SA-Seq2Seq is proposed,which uses the sequence-to-sequence model with attention mechanism.The model SA-Seq2Seq applies the pre-training model called BERT to introduce source text in the encoder so that each word contains the overall semantic information and uses gold summary as the target semantic information in the decoder to calculate the semantic inconsistency loss,thus ensuring the semantic integrity of the generated summary.Experiments are carried out on the dataset using the Chinese short text summary dataset LCSTS.The experimental results show that the model SA-Seq2Seq on the evaluation metric ROUGE is significantly improved compared to the benchmark model,and its ROUGE-1,ROUGE-2 and ROUGE-L scores increase by 3.4%,7.1%and 6.1%respectively in the dataset that is processed based on character and increase by 2.7%,5.4%and 11.7%respectively in the dataset that is processed based on word.So the SA-Seq2Seq model can effectively integrate Chinese short text and ensure the fluency and consistency of the generated summary,which can be applied to the Chinese short text summary generation task.