On-line news, social networks’ posts, e-mails, Wikipedia pages, .... the amount of information available on-line is growing at unprecedented rates. In this context of information overload, automatic text summarization and information extraction are key Natural Language Processing (NLP) techniques for extracting the most salient information from a document in order to populate knowledge repositories or provide users with brief stand-alone document surrogates.

Besides the amount of text we have to deal with daily, there is a further problem: certain types of texts such as news, which are produced for a general audience, may be too difficult to read and understand by some people, because these texts have a complex vocabulary and grammar. In this context, automatic text simplification is another NLP technique which aims at transforming a text into an equivalent which, using a simpler vocabulary or syntax, would be easier to read and understand.

Over the past years, we have been working on two lines of research; on the one hand, we have investigated ways to extract and condense the essential information from massive scientific literature and, on the other hand, we have carried out research to produce multilingual, adaptable, text simplification solutions for digital inclusion. In this seminar, I will take the opportunity to present the work carried out on our research lab on these two distinctive, exciting, and relevant topics in the information society