  Recent models in developing summarization systems consist of millions of
parameters and the model performance is highly dependent on the abundance of
training data. While most existing summarization corpora contain data in the
order of thousands to one million, generation of large-scale summarization
datasets in order of couple of millions is yet to be explored. Practically,
more data is better at generalizing the training patterns to unseen data. In
this paper, we introduce TLDR9+ -- a large-scale summarization dataset --
containing over 9 million training instances extracted from Reddit discussion
forum (https://github.com/sajastu/reddit_collector). This dataset is
specifically gathered to perform extreme summarization (i.e., generating
one-sentence summary in high compression and abstraction) and is more than
twice larger than the previously proposed dataset. We go one step further and
with the help of human annotations, we distill a more fine-grained dataset by
sampling High-Quality instances from TLDR9+ and call it TLDRHQ dataset. We
further pinpoint different state-of-the-art summarization models on our
proposed datasets.
