MultiLing 2019: Revision
News:
(2019-05-01) Training data for the Headline Generation community task published.
(2019-05-01) Training data for the Financial Narrative Summarization community task published.
(2019-04-03) Workshop date fixed to the 6th of September.
(2019-03-26) Call for participation in the community tasks published.
(2019-03-26) First call for papers published.
(2019-02-05) MultiLing accepted as a RANLP 2019 workshop.
Main Info:
Multiling 2019 is an accepted RANLP 2019 workshop and will take place at the 6th of September at Varna, Bulgaria.
Be sure to check the roadmap page for important dates.
Invited speakers:
Horacio Saggion - Universitat Pompeu Fabra
MultiLing 2019 covers a variety of topics on Natural Language Processing, focused on the multi-lingual aspect of summarization, but also its value across different settings:
Multilingual summarization across genres and sources:
Summarization has been receiving increasing attention during the last years. This is mostly due to the increasing volume and redundancy of available online information but also due to the user created content. Recently, more and more interest arises for methods that will be able to function on a variety of languages and across different types of content and genres (news, social media, transcripts).
This topic of research is mapped to different community tasks, covering different genres and
source types: Multilingual single-document summarization [Giannakopoulos et al., 2015]; news
headline generation (new task in MultiLing 2019); financial narrative summarization (new task
in MultiLing 2019, under a view of synergy and complementary to other workshops as in FNP
2018 at LREC’18 [El-Haj et al., 2018]); user-supplied comments summarization (OnForumS
task [Kabadjov et al., 2015]); conversation transcripts summarization (see also [Favre et al.,
2015]). The spectrum of the tasks covers a variety of real settings, identifying individual re-
quirements and intricacies, similarly to previous MultiLing endeavours [Giannakopoulos et al.,
2011, Giannakopoulos, 2013, Elhadad et al., 2013, Giannakopoulos et al., 2015, Giannakopoulos
et al., 2017].
Multilingual summary evaluation:
Summary evaluation has been an open question for several years, even though there exist methods that correlate well to human judgement, when called upon to compare systems. In the multilingual setting, it is not obvious that these methods will perform equally well to the English language setting. In fact, some preliminary results have shown that several problems may arise in the multilingual setting [Giannakopoulos et al., 2011].
The same challenges arise across different source types and genres. This section of the workshop
aims to cover and discuss these research problems and corresponding solutions.
Community tasks:
MultiLing is supported by a number of community tasks (check the related links on the top menu of the website for information on previous runs). This year we plan to implement 3 main tasks:
Stay tuned for more information on the tasks.
Programme Committee Members: (to be finalized)
John M. Conroy - IDA Center for Computing Sciences
Marina Litvak - Sami Shamoon College of Engineering
Udo Kruschwitz - University of Essex
Horacio Saggion - Universitat Pompeu Fabra
Elena Lloret - University of Alicante
Vangelis Karkaletsis - NCSR Demokritos
Mark Last - Ben-Gurion University of the Negev
Natalia Vanetik - Sami Shamoon College of Engineering; Ben-Gurion University of the Negev
Josef Steinberger - University of West Bohemia
Laura Plaza - UNED
Florian Boudin - University of Nantes
Iraklis Varlamis - Harokopio University
George Petasis - NCSR Demokritos
Peter Rankel - Stratus Solutions
George Giannakopoulos - NCSR Demokritos
Mahmoud El-Haj - Lancaster University
Ahmet Aker - University of Sheffield
Giuseppe Riccardi - University of Trento
Corina Forascu - University "Al. I. Cuza" Iasi
Michalis Vazirgiannis - Ecole Polytechnique; Athens University of Economics and Business
To stay updated on MultiLing2019, you can join the mailing list: multiling19 at scify dot org.
