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ML-TEXTSUM

Multi-language text summarization

Total Cost €

0

EC-Contrib. €

0

Partnership

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Project "ML-TEXTSUM" data sheet

The following table provides information about the project.

Coordinator
EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH 

Organization address
address: Raemistrasse 101
city: ZUERICH
postcode: 8092
website: https://www.ethz.ch/de.html

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Switzerland [CH]
 Project website http://fa.bianp.net/pages/mltextsum.html
 Total cost 265˙840 €
 EC max contribution 265˙840 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-GF
 Starting year 2017
 Duration (year-month-day) from 2017-09-01   to  2020-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH CH (ZUERICH) coordinator 265˙840.00
2    THE REGENTS OF THE UNIVERSITY OF CALIFORNIA US (OAKLAND CA) partner 0.00

Map

 Project objective

In our daily life, we are submerged by huge amounts of text, coming from different sources such as emails, news, reports, and so on. The availability of unprecedented volumes of data represents both a challenge and an opportunity. On one hand, it can lead to information overload, a phenomenon that limits one’s capacity to understand an issue and act in the presence of too much information. On the other hand, the effective harnessing of this information has undeniable economical potential. Furthermore, In the European context, special needs to be put to multilingualism to guarantee global access to high quality information.

The objective of this application is to develop ML-TEXTSUM, a system for efficient and accurate multi-lingual text summarization. That is, given as input a text document, the system will output a summary of the document in the same or in a different language. Building on recent breakouts in machine learning and natural language processing, I propose a novel architecture for ML-TEXTSUM that will be able to produce high quality summaries while at same time remain modular enough so that new languages can be added with minimal effort. The availability of such system shall allow citizens, regardless of their language, to better handle the information overload and to gain access to critically distilled information (e.g., what is a certain newspaper’s opinion on the same topic this year? Are male/female athletes portrayed differently by the media?).

The project is characterized by the interplay of multiple disciplines: the proposed architecture requires to master a combination of natural language processing and machine learning techniques. At the same time, the formidable scale of this system will require the development of novel distributed optimization methods. This interplay will be achieved thanks to my past and future collaborations, my solid background in optimization and machine learning, as well as through the acquisition of new ad-hoc skills.

 Publications

year authors and title journal last update
List of publications.
2018 Gidel, Gauthier; Pedregosa, Fabian; Lacoste-Julien, Simon
Frank-Wolfe Splitting via Augmented Lagrangian Method
published pages: 1456-1465, ISSN: , DOI:
Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics 84 2019-07-18
2018 Pedregosa, Fabian; Fatras, Kilian; Casotto, Mattia
Variance Reduced Three Operator Splitting
published pages: , ISSN: , DOI:
ArXiv preprint 1 2019-07-18
2018 Remi Leblond, Fabian Pedregosa, Simon Lacoste-Julien
Improved asynchronous parallel optimization analysis for stochastic incremental methods
published pages: , ISSN: 1533-7928, DOI:
Journal of Machine Learning Research 2019-07-18
2018 Pedregosa, Fabian; Askari, Armin; Negiar, Geoffrey; Jaggi, Martin
Step-Size Adaptivity in Projection-Free Optimization
published pages: , ISSN: , DOI:
ArXiv preprint 1 2019-07-18
2018 Kerdreux, Thomas; Pedregosa, Fabian; d\'Aspremont, Alexandre
Frank-Wolfe with Subsampling Oracle
published pages: , ISSN: , DOI:
Proceedings of the 35th International Conference on Machine Learning 2019-07-18
2018 Pedregosa, Fabian; Gidel, Gauthier
Adaptive Three Operator Splitting
published pages: 4085-4094, ISSN: , DOI:
Proceedings of the 35th International Conference on Machine Learning 3 2019-07-18

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The information about "ML-TEXTSUM" are provided by the European Opendata Portal: CORDIS opendata.

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