A Survey of Paraphrasing and Textual Entailment Methods

TL;DR

This survey reviews paraphrasing and textual entailment methods, highlighting their similarities, applications, and key techniques in natural language processing tasks such as question answering and summarization.

Contribution

It provides a comprehensive overview of methods, resources, and key ideas in paraphrasing and textual entailment, emphasizing their interconnectedness and applications.

Findings

Paraphrasing and textual entailment are closely related and often share techniques.

Both methods are crucial for various NLP applications like question answering and summarization.

The survey identifies prominent articles and resources in the field.

Abstract

Paraphrasing methods recognize, generate, or extract phrases, sentences, or longer natural language expressions that convey almost the same information. Textual entailment methods, on the other hand, recognize, generate, or extract pairs of natural language expressions, such that a human who reads (and trusts) the first element of a pair would most likely infer that the other element is also true. Paraphrasing can be seen as bidirectional textual entailment and methods from the two areas are often similar. Both kinds of methods are useful, at least in principle, in a wide range of natural language processing applications, including question answering, summarization, text generation, and machine translation. We summarize key ideas from the two areas by considering in turn recognition, generation, and extraction methods, also pointing to prominent articles and resources.