SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation (2110.10774v1)

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 \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.

• The paper presents the SciXGen dataset with 205,304 annotated scientific papers for context-aware text generation.
• It defines tasks like context-aware description and paragraph generation using detailed metadata and advanced LaTeX parsing.
• Experiments show that models using complete context outperform truncated approaches, highlighting benefits for automated academic writing.

SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation

The paper "SciXGen: A Scientific Paper Dataset for Context-Aware Text Generation" introduces a robust dataset designed to enhance the generation of context-aware scientific texts. The authors, Hong Chen, Hiroya Takamura, and Hideki Nakayama, present a new task in text generation, emphasizing the importance of contextual information. Their contribution is encapsulated in the SciXGen dataset, consisting of 205,304 well-annotated scientific papers.

Motivation and Task Definition

The task of generating scientific text involves not just understanding the input material but also leveraging external contextual information. This paper proposes context-aware text generation as a distinct task, where models are tasked with generating text based on a given context. The research specifically defines two sub-tasks: context-aware description generation, where text is generated for objects like tables and figures considering the body text context, and context-aware paragraph generation, where paragraphs are generated using cited papers as context.

Dataset Construction and Features

SciXGen stands out by offering comprehensive contextual data extracted from scientific papers. The authors use state-of-the-art tools, notably LaTeXML and an auxiliary LaTeX parser, to parse 225,495 papers, eventually curating 205,304 high-quality examples. The dataset includes diverse objects such as tables, figures, algorithms, and theorems. Unlike previous datasets that often lacked complete references to these objects, SciXGen ensures all are machine-readable and contextually linked.

The dataset's meticulous construction process ensures it retains intricate details, vital for generating context-rich text. The authors have also integrated metadata linking bibliographies to full-text papers from external sources, notably S2ORC, enhancing dataset utility.

Experiments and Results

The experiments focus on two tasks: context-aware description generation and paragraph generation. Various baseline models, including novel applications like Ordering-sensitive Fusion-in-Decoder (OFiD) and Retrieval-Augmented Generator (RAG-sequence), are evaluated. OFiD particularly demonstrates the importance of maintaining the order of retrieved sentences for effective description generation.

The results reveal that models leveraging complete context perform better than those relying solely on objects or body text, reinforcing the need for contextual awareness. Comparisons with models using truncated context demonstrate similar findings, albeit with computational efficiency concerns.

Implications and Future Directions

The implications of this research are significant for the field of AI-driven scientific text generation. By providing a dataset that incorporates comprehensive contextual information, SciXGen can serve as a cornerstone for developing models capable of generating scientifically plausible texts. This, in turn, could lead to advancements in automated literature reviews, enhanced paper summarizations, and more robust academic writing assistants.

Future developments may involve further refining context retrieval mechanisms and expanding the dataset to other scientific fields. Additionally, exploring multimodal approaches that align text generation with visual data presents a promising avenue for future research.

In summary, SciXGen represents a significant step forward in the context-aware generation of scientific texts. By meticulously curating a richly annotated dataset, the authors provide a powerful tool for advancing research in automated academic writing, positioning SciXGen as a critical asset for future investigations in AI-driven text generation.