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Found 14 papers, 3 papers with code
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.
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.
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.
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.
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.
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.
Does Pretraining for Summarization Require Knowledge Transfer?
Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization.
Downstream Datasets Make Surprisingly Good Pretraining Corpora
In experiments addressing both ELECTRA and RoBERTa models and 10 distinct downstream classification datasets, we observe that self-pretraining rivals standard pretraining on the BookWiki corpus (despite using around $10\times$--$500\times$ less data), outperforming the latter on $7$ and $5$ datasets, respectively.
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.
Abstractive Text Summarization
Out-of-Distribution Detection
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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.
USB: A Unified Summarization Benchmark Across Tasks and Domains
While the NLP community has produced numerous summarization benchmarks, none provide the rich annotations required to simultaneously address many important problems related to control and reliability.
Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains
We analyze whether the prevalence of a given domain in the pretraining corpus affects extractiveness and faithfulness of generated summaries of articles in this domain.
GenAudit: Fixing Factual Errors in Language Model Outputs with Evidence
We present GenAudit -- a tool intended to assist fact-checking LLM responses for document-grounded tasks.
