Question: What dataset do they use?

Evidence:
raw_evidence: We train language models on two languages: One model is estimated on the German newscrawl distributed by WMT'18 comprising 260M sentences or 6B tokens. Another model is trained on the English newscrawl data comprising 193M sentences or 5B tokens. We learn a joint Byte-Pair-Encoding (BPE; Sennrich et al., 2016) vocabulary of 37K types on the German and English newscrawl and train the language models with this vocabulary.
We consider two benchmarks: Most experiments are run on the WMT'18 English-German (en-de) news translation task and we validate our findings on the WMT'18 English-Turkish (en-tr) news task. For WMT'18 English-German, the training corpus consists of all available bitext excluding the ParaCrawl corpus and we remove sentences longer than 250 tokens as well as sentence-pairs with a source/target length ratio exceeding 1.5. This results in 5.18M sentence pairs. We tokenize all data with the Moses tokenizer BIBREF8 and apply the BPE vocabulary learned on the monolingual corpora.
raw_evidence: We train language models on two languages: One model is estimated on the German newscrawl distributed by WMT'18 comprising 260M sentences or 6B tokens. Another model is trained on the English newscrawl data comprising 193M sentences or 5B tokens. We learn a joint Byte-Pair-Encoding (BPE; Sennrich et al., 2016) vocabulary of 37K types on the German and English newscrawl and train the language models with this vocabulary.
We consider two benchmarks: Most experiments are run on the WMT'18 English-German (en-de) news translation task and we validate our findings on the WMT'18 English-Turkish (en-tr) news task. For WMT'18 English-German, the training corpus consists of all available bitext excluding the ParaCrawl corpus and we remove sentences longer than 250 tokens as well as sentence-pairs with a source/target length ratio exceeding 1.5. This results in 5.18M sentence pairs. We tokenize all data with the Moses tokenizer BIBREF8 and apply the BPE vocabulary learned on the monolingual corpora.
For WMT'18 English-Turkish, we use all of the available bitext comprising 208K sentence-pairs without any filtering. We develop on newstest2017 and test on newstest2018. For en-tr we only experiment with adding representations to the encoder and therefore apply the language model vocabulary to the source side. For the target vocabulary we learn a BPE code with 32K merge operations on the Turkish side of the bitext. Both datasets are evaluated in terms of case-sensitive de-tokenized BLEU BIBREF9 , BIBREF10 .
We consider the abstractive document summarization task comprising over 280K news articles paired with multi-sentence summaries. is a widely used dataset for abstractive text summarization. Following BIBREF11 , we report results on the non-anonymized version of rather than the entity-anonymized version BIBREF12 , BIBREF13 because the language model was trained on full text. Articles are truncated to 400 tokens BIBREF11 and we use a BPE vocabulary of 32K types BIBREF14 . We evaluate in terms of F1-Rouge, that is Rouge-1, Rouge-2 and Rouge-L BIBREF15 .

Answers:
German newscrawl distributed by WMT'18 , English newscrawl data, WMT'18 English-German (en-de) news translation task , WMT'18 English-Turkish (en-tr) news task
German newscrawl, English newscrawl, WMT'18 English-German (en-de) news, WMT'18 English-Turkish (en-tr) news task, WMT'18 English-German (en-de) news translation task and we validate our findings on the WMT'18 English-Turkish (en-tr) news task