MultiLing 2015: Multilingual Summarization of Single and Multi-Documents, On-line Fora, and Call-center Conversations
By George Giannakopoulos, Jeff Kubina, John M. Conroy, Josef Steinberger, Benoit Favre, Mijail Alexandrov Kabadjov, Udo Kruschwitz, Massimo Poesio. 2015
76 citations recorded by the source corpus.
In this paper we present an overview of MultiLing 2015, a special session at SIGdial 2015. MultiLing is a communitydriven initiative that pushes the state-ofthe-art in Automatic Summarization by providing data sets and fostering further research and development of summarization systems. There were in total 23 participants this year submitting their system outputs to one or more of the four tasks of MultiLing: MSS, MMS, OnForumS and CCCS. We provide a brief overview of each task and its participation and evaluation.
Subfields: Artificial Intelligence, Natural Language Processing, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Elizabeth Clark
- Sebastian Gehrmann
- Thibault Sellam
- Thomas Scialom
- Claire Cardie
- Jacopo Staiano
- Benjamin Piwowarski
- Sylvain Lamprier
In Natural Language Processing
- Elizabeth Clark
- Thibault Sellam
- Sebastian Gehrmann
- Thomas Scialom
- Faisal Ladhak
- Jacopo Staiano
- Sylvain Lamprier
- Benjamin Piwowarski
In Evaluation & Benchmarks
- Sebastian Gehrmann
- Elizabeth Clark
- Thibault Sellam
- Faisal Ladhak
- K. McKeown
- Claire Cardie
- Esin Durmus
- Tahmid Hasan
Citations in the corpus
- Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, background): “Additionally, only two of the 27 datasets were nonEnglish, despite much recent work that introduces multilingual summarization corpora (Giannakopoulos et al., 2015; Scialom et al., 2020; Ladhak et al., 2020; Hasan et al., 2021; Perez-Beltrachini and Lapata, 2021).”
- WikiLingua: A New Benchmark Dataset for Multilingual Abstractive Summarization (2020, background): “For MultiLing’15, an additional 30 documents were collected for evaluation purposes (Giannakopoulos et al., 2015).”
- MLSUM: The Multilingual Summarization Corpus (2020, background): “While the MultiLing benchmark covers 40 languages, it provides relatively few examples (10k in the 2019 release).”
- XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages (2021, background): “Giannakopoulos et al. (2015) introduced MultiL-ing 2015, a summarization dataset spanning 40 languages.”
- Language Agnostic Automatic Summarization Evaluation (2020, background): “This score corresponds to low ROUGE Re-call scores Giannakopoulos et al. (2015) report but is not in line with our language agnostic evaluation in section 4.1.”