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Found 9 papers, 1 papers with code
News Article Teaser Tweets and How to Generate Them
In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers.
A Hierarchical Decoder with Three-level Hierarchical Attention to Generate Abstractive Summaries of Interleaved Texts
Interleaved texts, where posts belonging to different threads occur in one sequence, are a common occurrence, e. g., online chat conversations.
Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data
Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions.
Generating Multi-Sentence Abstractive Summaries of Interleaved Texts
The interleaved posts are encoded hierarchically, i. e., word-to-word (words in a post) followed by post-to-post (posts in a channel).
Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization
A cascade of tasks are required to automatically generate an abstractive summary of the typical information-rich radiology report.
RadLing: Towards Efficient Radiology Report Understanding
Most natural language tasks in the radiology domain use language models pre-trained on biomedical corpus.
shs-nlp at RadSum23: Domain-Adaptive Pre-training of Instruction-tuned LLMs for Radiology Report Impression Generation
Instruction-tuned generative Large language models (LLMs) like ChatGPT and Bloomz possess excellent generalization abilities, but they face limitations in understanding radiology reports, particularly in the task of generating the IMPRESSIONS section from the FINDINGS section.
Generation of Radiology Findings in Chest X-Ray by Leveraging Collaborative Knowledge
Among all the sub-sections in a typical radiology report, the Clinical Indications, Findings, and Impression often reflect important details about the health status of a patient.
General-Purpose vs. Domain-Adapted Large Language Models for Extraction of Structured Data from Chest Radiology Reports
F1 score for extraction was 97% for RadLing-based system and 78% for GPT-4 system.
