Generating Wikipedia by Summarizing Long Sequences
Table 1: Order of magnitude input/output sizes and unigram recall for summarization datasets.
['[BOLD] Dataset', '[BOLD] Input', '[BOLD] Output', '[BOLD] # examples', '[BOLD] ROUGE-1 R']
In our work we also train neural abstractive models, but in the multi-document regime with Wikipedia. However, the input documents may consist of documents of arbitrary style originating from arbitrary sources. A higher score corresponds to a dataset more amenable to extractive summarization. In particular, if the output is completely embedded somewhere in the input (e.g. a wiki-clone), the score would be 100. Given a score of only 59.2 compared to 76.1 and 78.7 for other summarization datasets shows that ours is the least amenable to purely extractive methods.