Text summarization with pretrained encoders
Bidirectional Encoder Representations from Transformers (BERT) represents the latest
incarnation of pretrained language models which have recently advanced a wide range of …
incarnation of pretrained language models which have recently advanced a wide range of …
QuestEval: Summarization asks for fact-based evaluation
Summarization evaluation remains an open research problem: current metrics such as
ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate …
ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate …
Pretraining-based natural language generation for text summarization
H Zhang, J Xu, J Wang - arXiv preprint arXiv:1902.09243, 2019 - arxiv.org
In this paper, we propose a novel pretraining-based encoder-decoder framework, which can
generate the output sequence based on the input sequence in a two-stage manner. For the …
generate the output sequence based on the input sequence in a two-stage manner. For the …
Extractive summarization of long documents by combining global and local context
W Xiao, G Carenini - arXiv preprint arXiv:1909.08089, 2019 - arxiv.org
In this paper, we propose a novel neural single document extractive summarization model
for long documents, incorporating both the global context of the whole document and the …
for long documents, incorporating both the global context of the whole document and the …
Leveraging graph to improve abstractive multi-document summarization
Graphs that capture relations between textual units have great benefits for detecting salient
information from multiple documents and generating overall coherent summaries. In this …
information from multiple documents and generating overall coherent summaries. In this …
Conditional generation with a question-answering blueprint
The ability to convey relevant and faithful information is critical for many tasks in conditional
generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal …
generation and yet remains elusive for neural seq-to-seq models whose outputs often reveal …
Single-Document Abstractive Text Summarization: A Systematic Literature Review
Abstractive text summarization is a task in natural language processing that automatically
generates the summary from the source document in a human-written form with minimal loss …
generates the summary from the source document in a human-written form with minimal loss …
Sample efficient text summarization using a single pre-trained transformer
Language model (LM) pre-training has resulted in impressive performance and sample
efficiency on a variety of language understanding tasks. However, it remains unclear how to …
efficiency on a variety of language understanding tasks. However, it remains unclear how to …
BASS: Boosting abstractive summarization with unified semantic graph
Abstractive summarization for long-document or multi-document remains challenging for the
Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text …
Seq2Seq architecture, as Seq2Seq is not good at analyzing long-distance relations in text …
Generating representative headlines for news stories
Millions of news articles are published online every day, which can be overwhelming for
readers to follow. Grouping articles that are reporting the same event into news stories is a …
readers to follow. Grouping articles that are reporting the same event into news stories is a …