Challenge: Annotating scientific literature directly on PDF documents can greatly improve the labeling efficiency of scientists whose annotation costs are very high.
Approach: They propose an integrated onsite scientific literature annotation tool for natural scientists and Natural Language Processing (NLP) researchers.
Outcome: The proposed tool supports the whole lifecycle of corpus generation including i)project management, ii)resource management, and iv)ontology management, as well as manual annotation, onsite auto annotation, and vi)task statistic.

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Embedding-based Scientific Literature Discovery in a Text Editor Application (2020.acl-demos)

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Challenge: Despite the availability of powerful search engines and text editing software, discovering relevant papers and integrating the knowledge into a manuscript remain complex tasks associated with high cognitive load.
Approach: They propose to combine text editing and literature discovery in an interactive user interface with a search engine that couples Boolean keyword filtering with nearest neighbor search over text embeddings.
Outcome: The proposed application combines text editing and literature discovery in an interactive user interface.
FITAnnotator: A Flexible and Intelligent Text Annotation System (2021.naacl-demos)

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Challenge: In this paper, we introduce FITAnnotator, a generic web-based tool for efficient text annotation.
Approach: They propose a generic web-based tool for efficient text annotation.
Outcome: The proposed tool is based on a fully modular architecture and provides three kinds of interfaces to annotate instances, evaluate annotation quality and manage the annotation task for annotators, reviewers and managers.
SciLit: A Platform for Joint Scientific Literature Discovery, Summarization and Citation Generation (2023.acl-demo)

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Challenge: Scientific writing involves retrieving, summarizing, and citing relevant papers.
Approach: They propose a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper.
Outcome: The proposed pipeline recommends relevant papers from large databases of hundreds of millions of papers . it provides extractive summaries and abstractively-generated citation sentences . authors question whether it is possible to partly automate this process to reduce cognitive load .
TextAnnotator: A UIMA Based Tool for the Simultaneous and Collaborative Annotation of Texts (2020.lrec-1)

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Challenge: Existing annotation tools are not efficient for the annotation of corpora and are not error-free.
Approach: They propose to extend existing annotation tools by evaluating their flexibility and efficiency.
Outcome: The proposed system performs platform-independent multimodal annotations and annotates complex textual structures.
AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation (2026.eacl-tutorials)

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Challenge: This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process.
Approach: This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process.
Outcome: This tutorial provides an overview of recent advances in AI-assisted tools and models that support and enhance the scientific research process.
NLATool: an Application for Enhanced Deep Text Understanding (C18-2)

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Challenge: a wide range of subfields in natural language processing see systems solving their tasks with sufficiently high-quality levels.
Approach: They propose a web application that supports text annotation and enriches the text with additional information from a number of sources directly within the application.
Outcome: The proposed web application is based on a human-centered design process . it offers a rich visualization of texts and the entities mentioned in them through an easy to use interface.
Related Work and Citation Text Generation: A Survey (2024.emnlp-main)

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Challenge: Academic research paper authors must perform literature review to compare work with prior work . authors must compose coherent story that connects prior work and current work based on author's understanding of field .
Approach: They propose to use automatic related work generation (RWG) to generate papers . authors summarize key approaches and define tasks in a zoo of historical works .
Outcome: a new study summarises key approaches and defines the tasks and discusses the challenges of RWG.
BioReddit: Word Embeddings for User-Generated Biomedical NLP (D19-62)

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Challenge: a corpus of medical-themed posts was scrapped from Reddit to train word embeddings on downstream tasks.
Approach: They propose to train word embeddings from a corpus of medical forums from reddit scrapping posts from medical-themed subreddits.
Outcome: The proposed system outperforms embeddings trained on general purpose data or on scientific papers when applied on user-generated content.
Infrastructure for Semantic Annotation in the Genomics Domain (2020.lrec-1)

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Challenge: a novel infrastructure for biomedical text mining combines NLP and corpus linguistics methods to provide a comprehensive corpus for literature-based discovery.
Approach: They propose a novel pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature . it uses an updatable Gene Ontology Semantic Tagger and a NLP pipeline scheduler to collect and process the corpus.
Outcome: The proposed infrastructure allows for extreme-scale research on the open access PubMed Central archive.
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.

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