Semantic Sentence Embeddings for Paraphrasing and Text Summarization

TL;DR

This paper presents a new sentence embedding framework that captures semantic information effectively, enabling improved paraphrasing and summarization tasks in NLP.

Contribution

It introduces a sentence-to-vector encoding method trained on paraphrase pairs, applicable to multiple NLP tasks, enhancing semantic understanding.

Findings

Vectors encode sentences with similar semantics

Effective for paraphrasing and summarization

Provides insights into language embedding quality

Abstract

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.