Year: 2023
Type: conference-paper
Abstract: Entity-centric summarization is a form of controllable summarization that aims to generate a summary for a specific entity given a document.Concise summaries are valuable in various reallife applications, as they enable users to quickly grasp the main points of the document focusing on an entity of interest.This paper presents ENTSUMV2, a more abstractive version of the original entity-centric ENTSUM summarization dataset.In ENTSUMV2 the annotated summaries are intentionally made shorter to benefit more specific and useful entity-centric summaries for downstream users.We conduct extensive experiments on this dataset using multiple abstractive summarization approaches that employ supervised fine-tuning or large-scale instruction tuning.Additionally, we perform comprehensive human evaluation that incorporates metrics for measuring crucial facets.These metrics provide a more fine-grained interpretation of the current state-of-the-art systems and highlight areas for future improvement.
Institution: Amazon (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4389524318