S ci XG en: A Scientific Paper Dataset for Context-Aware Text Generation (2021)

Abstract: Generating texts in scientific papers requires not only capturing the content contained within the given input but also frequently acquiring the external information called context. We push forward the scientific text generation by proposing a new task, namely 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 Scientific Paper Dataset for ConteXt-Aware Text Generation (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.

## introduction text generation in the scientific domain has been increasingly received attention recently due to its wide range of applications such as summarization @xcite , paragraph generation @xcite and table description generation @xcite . though recent works have brought breakthroughs @xcite @xcite , how to faithfully generate texts/paragraphs remains challenging. as a case study shown in ## context-aware text generation we conduct the experiments on two primary tasks in context-aware text generation: context-aware description generation (i.e., generating description for paper objects such as tables, figures, algorithms and theorems given the body text as context), and the context-aware paragraph generation (i.e., generating a paragraph given cited papers as context). ## conclusion this paper addresses the novel yet challenging problem of context-aware text generation in the scientific domain. to promote this task, we present a novel large-scale scixgen dataset. we thoroughly investigate the efficacy of our dataset in two primary tasks: context-aware description generation and context-aware paragraph generation. despite achieving remarkable results in our experiments, using context in text generation still has room for improvement as above discussions. we believe that our dataset can serve as a valuable testbed for various tasks in scientific paper research, including summarization with full-text cited papers, and image-text multimodal text generation.