Chiruzzo L, AbuRa’ed A, Bravo A, Saggion H. LaSTUS-TALN+INCO @ CL-SciSumm 2019. 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019)
We have relevant datasets, repositories, frameworks and tools of relevance for research and technology transfer initiatives related to knowledge extraction. This section provides an overview on a selection of them and links to download or contact details.
The MdM Strategic Research Program has its own community in Zenodo for material available in this repository
as well as at the UPF e-repository 
. Below a non-exhaustive list of datasets representative of the research in the Department.
As part of the promotion of the availability of resources, the creation of specific communities in Zenodo has also been promoted, at level of research communities (for instance, MIR and Educational Data Analytics) or MSc programs (for instance, the Master in Sound and Music Computing)
Chiruzzo L, AbuRa’ed A, Bravo A, Saggion H. LaSTUS-TALN+INCO @ CL-SciSumm 2019. 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019)
Chiruzzo L, AbuRa’ed A, Bravo A, Saggion H. LaSTUS-TALN+INCO @ CL-SciSumm 2019. 4th Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2019)
In this paper we present several systems developed to participate in the 4th Computational Linguistics Scientific Document Summarization Shared challenge which addresses the problem of summarizing a scientific paper using information from its citation network (i.e., the papers that cite the given paper). Given a cluster of scientific documents where one is a reference paper (RP) and the remaining documents are papers citing the reference, two tasks are proposed: (i) to identify which sentences in the reference paper are being cited and why they are cited, and (ii) to produce a citation-based summary of the reference paper using the information in the cluster. Our systems are based on both supervised (LSTM and convolutional neural networks) and unsupervised techniques using word embedding representations and features computed from the linguistic and semantic analysis of the documents