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Found 11 papers, 1 papers with code
Improving the Robustness of Summarization Models by Detecting and Removing Input Noise
We present a large empirical study quantifying the sometimes severe loss in performance (up to 12 ROUGE-1 points) from different types of input noise for a range of datasets and model sizes.
Out-of-Distribution Detection and Selective Generation for Conditional Language Models
Furthermore, the space of potential low-quality outputs is larger as arbitrary text can be generated and it is important to know when to trust the generated output.
Downstream Datasets Make Surprisingly Good Pretraining Corpora
For most natural language processing tasks, the dominant practice is to finetune large pretrained transformer models (e. g., BERT) using smaller downstream datasets.
Does Pretraining for Summarization Require Knowledge Transfer?
Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization.
Extracting Structured Data from Physician-Patient Conversations By Predicting Noteworthy Utterances
In this exploratory study, we describe a new dataset consisting of conversation transcripts, post-visit summaries, corresponding supporting evidence (in the transcript), and structured labels.
Reinforced Rewards Framework for Text Style Transfer
Style transfer deals with the algorithms to transfer the stylistic properties of a piece of text into that of another while ensuring that the core content is preserved.
Generating SOAP Notes from Doctor-Patient Conversations Using Modular Summarization Techniques
Following each patient visit, physicians draft long semi-structured clinical summaries called SOAP notes.
Improving generation quality of pointer networks via guided attention
The first shortcoming is the extractive nature of the generated summaries, since the network eventually learns to copy from the input article most of the times, affecting the abstractive nature of the generated summaries.
Vocabulary Tailored Summary Generation
Neural sequence-to-sequence models have been successfully extended for summary generation. However, existing frameworks generate a single summary for a given input and do not tune the summaries towards any additional constraints/preferences.
Corpus-based Content Construction
Enterprise content writers are engaged in writing textual content for various purposes.
Generating Topic-Oriented Summaries Using Neural Attention
Existing summarization algorithms generate a single summary and are not capable of generating multiple summaries tuned to the interests of the readers.
