Task: Language Identification
Input (Spanish, English): This paper provides two RNN-based architectures for extractive document summarization. The first, "Classify", reads in the whole document and traverses the sentences a second time to decide whether to include them or not (0/1 decisions). The second, "Select",  reads in the whole document and picks the most relevant sentence one at the time. The models assume that oracle extractive summaries exist, and a pseudo ground-truth generation procedure is used, which mimics Svore et al. (2007) among others.   Overall, this paper seems a small increment over Cheng & Lapata (2016) and performance is similar or worse to that paper. The problem of single document extractive summarization is not particularly exciting since in DUC 2002 (14 years ago) existing models could not beat the lead baseline (which selects the first sentences of the document). It's a pity that this paper doesn't address the most interesting problems of abstractive summarization or apply the proposed approach to multi-document summarization. It's also a little disappointing that the maximum sentence length had to be capped to 50, which suggests the model has some trouble to scale.
Output (English): en