Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net
Xin Sheng, Linli Xu, Yinlong Xu, Deqiang Jiang, Bo Ren · Findings of the Association for Computational Linguistics: NAACL 2022 · 2022
We propose a novel siamese generative adver sarial net for abstractive text summarization (SSPGAN), which can preserve the main se mantics of the source text.Different from pre vious generative adversarial net based methods, SSPGAN is equipped with a siamese semanticpreserving discriminator, which can not only be trained to discriminate the machine-generated summaries from the human-summarized ones, but also ensure the semantic consistency be tween the source text and target summary.As a consequence of the min-max game between the generator and the siamese semantic-preserving discriminator, the generator can generate a sum mary that conveys the key content of the source text more accurately.Extensive experiments on several text summarization benchmarks in dif ferent languages demonstrate the effectiveness of the proposed method.Source: