In this paper, we present a large-scale Chinese news summarization dataset CNewSum, which consists of 304, 307 documents and human-written summaries for the news feed.
By sampling in the latent secondary structure space, we can generate peptides with ideal amino acids and secondary structures at the same time.
We introduce MTG, a new benchmark suite for training and evaluating multilingual text generation.
Previous work for text summarization in scientific domain mainly focused on the content of the input document, but seldom considering its citation network.
In this paper, we perform an in-depth analysis of characteristics of different datasets and investigate the performance of different summarization models under a cross-dataset setting, in which a summarizer trained on one corpus will be evaluated on a range of out-of-domain corpora.
An intuitive way is to put them in the graph-based neural network, which has a more complex structure for capturing inter-sentence relationships.
This paper creates a paradigm shift with regard to the way we build neural extractive summarization systems.
Ranked #1 on Text Summarization on BBC XSum
In this paper, we take stock of the current state of summarization datasets and explore how different factors of datasets influence the generalization behaviour of neural extractive summarization models.
Although domain shift has been well explored in many NLP applications, it still has received little attention in the domain of extractive text summarization.
The recent years have seen remarkable success in the use of deep neural networks on text summarization.
Ranked #6 on Extractive Text Summarization on CNN / Daily Mail