IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications (2209.13243v1)

Abstract: Understanding the origin and influence of the publication's idea is critical to conducting scientific research. However, the proliferation of scientific publications makes it difficult for researchers to sort out the evolution of all relevant literature. To this end, we present IdeaReader, a machine reading system that finds out which papers are most likely to inspire or be influenced by the target publication and summarizes the ideas of these papers in natural language. Specifically, IdeaReader first clusters the references and citations (first-order or higher-order) of the target publication, and the obtained clusters are regarded as the topics that inspire or are influenced by the target publication. It then picks out the important papers from each cluster to extract the skeleton of the idea flow. Finally, IdeaReader automatically generates a literature review of the important papers in each topic. Our system can help researchers gain insight into how scientific ideas flow from the target publication's references to citations by the automatically generated survey and the visualization of idea flow.

• The paper introduces IdeaReader, a system that automates literature review generation and maps the flow of ideas through candidate selection and clustering.
• IdeaReader employs advanced NLP techniques, including TF-IDF, Sentence-BERT, and graph algorithms like PageRank and ProNE, to assess relevance among publications.
• The system generates topic surveys by summarizing top papers with a fine-tuned BertSumABS model, helping researchers trace the evolution of ideas.

The paper introduces IdeaReader, a machine reading system designed to elucidate the flow of ideas within scientific publications. This system is engineered to discern which academic works are likely to inspire or be influenced by a given publication, and it provides a summarization of these relationships. The primary function of IdeaReader is to automate the generation of a literature review and offer a visualization of the idea flow associated with a target publication.

System Components and Methodology:

1. Candidate Paper Selection: IdeaReader begins by querying candidate reference and citation papers from the Acemap database. The system initially considers first-order reference papers of the target publication and extends the search to higher-order references if fewer than 100 are identified. PageRank is employed to select the top 100 papers amongst the candidates.
2. Paper Clustering and Relevance Scoring: The system utilizes a combination of TF-IDF and Sentence-BERT for encoding paper abstracts. It includes ProNE for incorporating embeddings based on the citation network. Papers are clustered using Kernel k-means, and each cluster signifies a topic relevant to the target publication. Relevance between papers and the target publication is quantified using the vector inner product and citation data, and papers are ranked by their relevance scores.
3. Survey Generation: For each cluster, IdeaReader generates a survey by summarizing the top five papers with the highest relevance scores. The summarization process involves automatic text annotation and utilizing BertSumABS for creating general sentences, fine-tuned with a related work generation dataset. SciBERT is also used to identify objective sentences from the abstracts, forming the basis of the summary sentences.
4. Front-End Interface:

The system houses a user interface featuring: - A target paper information panel with metadata and topic statistics. - Papers about papers that likely inspire or derive influence from the target paper. - A visual representation of the idea flow, termed the "Tracing and evolution tree," highlighting the directional flow of ideas.

Impact and Utility:

IdeaReader facilitates an understanding of the conceptual lineage of scientific publications, which is significant for both novice and experienced researchers. By automating the creation of literature surveys and tracking idea evolution, IdeaReader assists in identifying foundational and derivative works, pertinent for situating new research within the existing body of literature.

Technical Insights:

• The system leverages advanced natural language processing techniques and graph-based algorithms for clustering and summarization.
• It employs fine-tuned pre-trained models for the generation of coherent and contextually accurate summaries.
• The use of citation networks and algorithmic selections ensures relevance and quality in the identification of pertinent academic works.

By integrating these processes, IdeaReader streamlines the traditionally labor-intensive task of literature review, offering a succinct yet comprehensive depiction of scientific discourse related to any given publication. This system is especially pertinent in managing the burgeoning volume of scientific literature published across disciplines.