Summarization, Simplification, and Generation: The Case of Patents
It addresses the need for better NLP tools in the patent domain, which is crucial for R&D processes, but is incremental as it reviews existing work.
This survey examines NLP approaches for summarizing, simplifying, and generating patent text, highlighting the unique challenges patents pose to current systems and identifying gaps for future research.
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.