GrapAL: Connecting the Dots in Scientific Literature (P19-3)

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Challenge: Several software tools are available to help researchers perform tasks such as searching for papers, assessing applicants for a research position and keeping track of papers published on topics of interest.
Approach: They introduce a graph database of academic literature with an intuitive schema and query language . they open source the demo code to help other researchers develop applications that build on it .
Outcome: The proposed tool can be used to find experts on a given topic for peer review, find indirect connections between biomedical entities, and compute citation-based metrics.

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Construction of the Literature Graph in Semantic Scholar (N18-3)

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Challenge: Fig. 1 summarizes a scalable system for organizing published scientific literature into a heterogeneous graph . authors describe methods used to enable semantic features in www.semanticscholar.org .
Approach: They describe a scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery.
Outcome: The proposed system can be deployed on a scalable platform and report empirical results for each task.
NLP-KG: A System for Exploratory Search of Scientific Literature in Natural Language Processing (2024.acl-demos)

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Challenge: Existing systems for scientific literature search are typically tailored to keyword-based lookup searches, limiting possibilities for exploration.
Approach: They propose a feature-rich system that supports the exploration of research literature in unfamiliar natural language processing fields.
Outcome: The proposed system supports the exploration of research literature in unfamiliar natural language processing fields.
CitationIE: Leveraging the Citation Graph for Scientific Information Extraction (2021.acl-long)

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Challenge: Existing work on scientific information extraction (SciIE) considers extraction solely based on the content of an individual paper, without considering the paper’s place in the broader literature.
Approach: They propose to automate the extraction of key information from scientific documents by leveraging a complementary source: the citation graph of referential links between citing and cited papers.
Outcome: The proposed model improves on a set of English-language scientific documents.
Datasets for Scientific Literature Understanding: A Survey (2026.findings-acl)

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Challenge: Empowering machines to understand scientific literature is crucial for accelerating scientific discovery and advancing the AI for Science paradigm.
Approach: They propose a systematic taxonomy that organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning.
Outcome: The proposed taxonomy organizes resources spanning structural understanding, text understanding, multimodal understanding and pre-training/instruction fine-tuning.
Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature (2022.emnlp-main)

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Challenge: Existing datasets for lay summarisation are limited in size and scope, hindering the development of data-driven approaches.
Approach: They propose to use two new datasets for the lay summarisation of biomedical research articles to characterise their lay summaries.
Outcome: The proposed datasets are compared with existing datasets and show they can be leveraged to support different audiences and applications.
End-to-End Construction of NLP Knowledge Graph (2021.findings-acl)

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Challenge: a new schema for NLP knowledge about tasks, datasets and metrics is proposed.
Approach: They propose a new schema that represents knowledge about tasks, datasets and metrics in the NLP domain.
Outcome: The proposed framework can be automatically built into scientific leaderboards . the proposed system achieves reasonable results for all relation types on this small-scale graph .
Beyond Metadata: What Paper Authors Say About Corpora They Use (2021.findings-acl)

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Challenge: Currently, dataset retrieval relies almost exclusively on metadata provided by the publishers.
Approach: They propose to use metadata to extract review statements from scientific publications . they argue that a crucial piece of information is missing to inform the examination of search results .
Outcome: The proposed analysis is the first of its kind in the field of Natural Language Processing.
Descriptive Knowledge Graph in Biomedical Domain (2023.emnlp-demo)

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Challenge: Existing systems that retrieve unconnected passages do not provide efficient search for relational knowledge.
Approach: They propose a system that automatically extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates efficient search for relational knowledge.
Outcome: The proposed system extracts and generates informative and descriptive sentences from the biomedical corpus and facilitates the efficient search for relational knowledge.
A Multi-level Annotated Corpus of Scientific Papers for Scientific Document Summarization and Cross-document Relation Discovery (2020.lrec-1)

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Challenge: Recent studies have proposed to take advantage of the scientific paper's citation network to approach literature summarization.
Approach: They propose to annotate related work sections, cite papers and sentences using machine readable data and an additional layer of papers citing the references.
Outcome: The proposed corpus expands the existing data-set of related work sections and cites the papers cited in the related work section.
Text Annotation Graphs: Annotating Complex Natural Language Phenomena (L18-1)

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Challenge: Text Annotation Graphs is a web-based tool for annotating text . it provides functionality for representing complex relationships between words and word phrases .
Approach: They introduce a web-based tool for annotating text, Text Annotation Graphs, or TAG . it provides functionality for representing complex relationships between words and word phrases .
Outcome: The proposed software can represent complex relationships between words and words . it can also be used to find similar structures within the current document or external annotated documents.

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