SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation
Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called \textit{context}. We push forward the scientific text generation by proposing a new task, namely \textbf{context-aware text generation} in the scientific domain, aiming at exploiting the contributions of context in generated texts. To this end, we present a novel challenging large-scale \textbf{Sci}entific Paper Dataset for Conte\textbf{X}t-Aware Text \textbf{Gen}eration (SciXGen), consisting of well-annotated 205,304 papers with full references to widely-used objects (e.g., tables, figures, algorithms) in a paper. We comprehensively benchmark, using state-of-the-arts, the efficacy of our newly constructed SciXGen dataset in generating description and paragraph. Our dataset and benchmarks will be made publicly available to hopefully facilitate the scientific text generation research.
Code (0)
등록된 구현이 없습니다.
Tasks
Text GenerationSimilar Papers 제목 키워드 기반
Rethinking Scientific Summarization Evaluation: Grounding Explainable Metrics on Facet-aware Benchmark
The summarization capabilities of pretrained and large language models (LLMs) have been widely validated in general areas, but their use in scientific corpus, which involves complex sentences and specialized knowledge, h…
A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networks
With the tremendous growth in the number of scientific papers being published, searching for references while writing a scientific paper is a time-consuming process. A technique that could add a reference citation at the…
BenchmarkingCitation RecommendationSentenceTAGContrastive Hierarchical Discourse Graph for Scientific Document Summarization
The extended structural context has made scientific paper summarization a challenging task. This paper proposes CHANGES, a contrastive hierarchical graph neural network for extractive scientific paper summarization. CHAN…
Contrastive LearningDocument SummarizationGraph Neural NetworkScientific Document Summarization+1SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Models
Large Language Models (LLMs) have shown impressive capabilities in contextual understanding and reasoning. However, evaluating their performance across diverse scientific domains remains underexplored, as existing benchm…
Knowledge GraphsEnhancing Abstractive Summarization of Scientific Papers Using Structure Information
Abstractive summarization of scientific papers has always been a research focus, yet existing methods face two main challenges. First, most summarization models rely on Encoder-Decoder architectures that treat papers as …
Abstractive Text SummarizationFeature Engineering