# Semantic Sentence Embeddings for Paraphrasing and Text Summarization

arXiv: https://arxiv.org/abs/1809.10267 · HTML: https://mlsanity.com/paper/1809.10267/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2018.

## Built on by (most cited first, 3 of 3)

- [Knowledge-aware Document Summarization: A Survey of Knowledge, Embedding Methods and Architectures](https://mlsanity.com/paper/2204.11190/) (2022)
- [Semantic Representations of Mathematical Expressions in a Continuous Vector Space](https://mlsanity.com/paper/2211.08142/) (2022)
- [Neural paraphrasing by automatically crawled and aligned sentence pairs](https://mlsanity.com/paper/2402.10558/) (2024)

## Abstract (arXiv)

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.