SEMANTIC SENTENCE EMBEDDINGS FOR PARAPHRASING AND TEXT SUMMARIZATION

被引：0

作者：

Zhang, Chi ^{[1
]}

Sah, Shagan ^{[1
]}

Thang Nguyen ^{[1
]}

Peri, Dheeraj ^{[1
]}

Loui, Alexander ^{[2
]}

Salvaggio, Carl ^{[1
]}

Ptucha, Raymond ^{[1
]}

机构：

[1] Rochester Inst Technol, Rochester, NY 14623 USA

[2] Kodak Alaris Imaging Sci R&D, Rochester, NY 14615 USA

来源：

2017 IEEE GLOBAL CONFERENCE ON SIGNAL AND INFORMATION PROCESSING (GLOBALSIP 2017) | 2017年

关键词：

sentence embedding; sentence encoding; sentence paraphrasing; text summarization; deep learning;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper introduces a sentence to vector encoding framework suitable for advanced natural language processing. Our latent representation is shown to encode sentences with common semantic information with similar vector representations. The vector representation is extracted from an encoder-decoder model which is trained on sentence paraphrase pairs. We demonstrate the application of the sentence representations for two different tasks - sentence paraphrasing and paragraph summarization, making it attractive for commonly used recurrent frameworks that process text. Experimental results help gain insight how vector representations are suitable for advanced language embedding.

引用

页码：705 / 709

页数：5

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