Papers Abstract generation
“Abstract generation” 태그가 달린 논문 16편 · 필터 해제
RGL: A Graph-Centric, Modular Framework for Efficient Retrieval-Augmented Generation on Graphs
Recent advances in graph learning have paved the way for innovative retrieval-augmented generation (RAG) systems that leverage the inherent relational structures in graph data. However, many existing approaches suffer fr…
Abstract generationGraph LearningModality completion+3NSF-SciFy: Mining the NSF Awards Database for Scientific Claims
We present NSF-SciFy, a large-scale dataset for scientific claim extraction derived from the National Science Foundation (NSF) awards database, comprising over 400K grant abstracts spanning five decades. While previous d…
16kAbstract generationClaim VerificationNUTSHELL: A Dataset for Abstract Generation from Scientific Talks
Scientific communication is receiving increasing attention in natural language processing, especially to help researches access, summarize, and generate content. One emerging application in this area is Speech-to-Abstrac…
Abstract generationThe Master-Slave Encoder Model for Improving Patent Text Summarization: A New Approach to Combining Specifications and Claims
In order to solve the problem of insufficient generation quality caused by traditional patent text abstract generation models only originating from patent specifications, the problem of new terminology OOV caused by rapi…
Abstract generationText GenerationText SummarizationPatentEval: Understanding Errors in Patent Generation
In this work, we introduce a comprehensive error typology specifically designed for evaluating two distinct tasks in machine-generated patent texts: claims-to-abstract generation, and the generation of the next claim giv…
Abstract generationText GenerationDGoT: Dynamic Graph of Thoughts for Scientific Abstract Generation
The method of training language models based on domain datasets has obtained significant achievements in the task of generating scientific paper abstracts. However, such models face problems of generalization and expensi…
Abstract generationHallucinationAcademicGPT: Empowering Academic Research
Large Language Models (LLMs) have demonstrated exceptional capabilities across various natural language processing tasks. Yet, many of these advanced LLMs are tailored for broad, general-purpose applications. In this tec…
Abstract generationGeneral KnowledgeMMLUQuestion AnsweringTransformer Based Implementation for Automatic Book Summarization
Document Summarization is the procedure of generating a meaningful and concise summary of a given document with the inclusion of relevant and topic-important points. There are two approaches: one is picking up the most r…
Abstract generationAbstractive Text SummarizationBook summarizationDocument Summarization+1UPER: Boosting Multi-Document Summarization with an Unsupervised Prompt-based Extractor
Multi-Document Summarization (MDS) commonly employs the 2-stage extract-then-abstract paradigm, which first extracts a relatively short meta-document, then feeds it into the deep neural networks to generate an abstract. …
Abstract generationDocument SummarizationLanguage ModelingLanguage Modelling+1TWAG: A Topic-Guided Wikipedia Abstract Generator
Wikipedia abstract generation aims to distill a Wikipedia abstract from web sources and has met significant success by adopting multi-document summarization techniques. However, previous works generally view the abstract…
Abstract generationArticlesDocument SummarizationMulti-Document Summarization+1CBAG: Conditional Biomedical Abstract Generation
Biomedical research papers use significantly different language and jargon when compared to typical English text, which reduces the utility of pre-trained NLP models in this domain. Meanwhile Medline, a database of biome…
Abstract generationDescriptiveLanguage ModelingLanguage Modelling+1An Entity-Driven Framework for Abstractive Summarization
Abstractive summarization systems aim to produce more coherent and concise summaries than their extractive counterparts. Popular neural models have achieved impressive results for single-document summarization, yet their…
Abstract generationAbstractive Text SummarizationDocument SummarizationReinforcement Learning+1Sentence-Level Content Planning and Style Specification for Neural Text Generation
Building effective text generation systems requires three critical components: content selection, text planning, and surface realization, and traditionally they are tackled as separate problems. Recent all-in-one style n…
Abstract generationArticlesDecoderSentence+1Neural Network-Based Abstract Generation for Opinions and Arguments
We study the problem of generating abstractive summaries for opinionated text. We propose an attention-based neural network model that is able to absorb information from multiple text units to construct informative, conc…
Abstract generationExtractive Summarization