We survey Natural Language Processing (NLP) approaches to summarizing, simplifying, and generating patents’ text. While solving these tasks has important practical applications – given patents’ centrality in the R&D process – patents’ idiosyncrasies open peculiar challenges to the current NLP state of the art. This survey aims at (a) describing patents’ characteristics and the questions they raise to the current NLP systems, (b) critically presenting previous work and its evolution, and (c) drawing attention to directions of research in which further work is needed. To the best of our knowledge, this is the first survey of generative approaches in the patent domain.

Summarization, simplification, and generation: The case of patents

Casola, Silvia
;
Lavelli, Alberto
2022-01-01

Abstract

We survey Natural Language Processing (NLP) approaches to summarizing, simplifying, and generating patents’ text. While solving these tasks has important practical applications – given patents’ centrality in the R&D process – patents’ idiosyncrasies open peculiar challenges to the current NLP state of the art. This survey aims at (a) describing patents’ characteristics and the questions they raise to the current NLP systems, (b) critically presenting previous work and its evolution, and (c) drawing attention to directions of research in which further work is needed. To the best of our knowledge, this is the first survey of generative approaches in the patent domain.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11582/332547
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact