# A Search Engine for Discovery of Scientific Challenges and Directions

arXiv: https://arxiv.org/abs/2108.13751 · HTML: https://mlsanity.com/paper/2108.13751/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 16 of 16)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [CORD-19: The COVID-19 Open Research Dataset](https://mlsanity.com/paper/2004.10706/) (2020)
- [With Little Power Comes Great Responsibility](https://mlsanity.com/paper/2010.06595/) (2020)
- [PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts](https://mlsanity.com/paper/1710.06071/) (2017)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts](https://mlsanity.com/paper/1902.09476/) (2019)
- [Accelerating Innovation Through Analogy Mining](https://mlsanity.com/paper/1706.05585/) (2017)
- [Syntactic Search by Example](https://mlsanity.com/paper/2006.03010/) (2020)
- [SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search](https://mlsanity.com/paper/2005.12668/) (2020)
- [Extracting a Knowledge Base of Mechanisms from COVID-19 Papers](https://mlsanity.com/paper/2010.03824/) (2020)
- [Interactive Extractive Search over Biomedical Corpora](https://mlsanity.com/paper/2006.04148/) (2020)

## Built on by (most cited first, 12 of 12)

- [Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity](https://mlsanity.com/paper/2111.08366/) (2021)
- [A Computational Inflection for Scientific Discovery](https://mlsanity.com/paper/2205.02007/) (2022)
- [PubMed and Beyond: Biomedical Literature Search in the Age of Artificial Intelligence](https://mlsanity.com/paper/2307.09683/) (2023)
- [A Survey on Human-Centric LLMs](https://mlsanity.com/paper/2411.14491/) (2024)
- [DiscipLink: Unfolding Interdisciplinary Information Seeking Process via Human-AI Co-Exploration](https://mlsanity.com/paper/2408.00447/) (2024)
- [Exploring and Verbalizing Academic Ideas by Concept Co-occurrence](https://mlsanity.com/paper/2306.02282/) (2023)
- [Beyond Good Intentions: Reporting the Research Landscape of NLP for Social Good](https://mlsanity.com/paper/2305.05471/) (2023)
- [Clinical Evidence Engine: Proof-of-Concept For A Clinical-Domain-Agnostic Decision Support Infrastructure](https://mlsanity.com/paper/2111.00621/) (2021)
- [Good Data, Large Data, or No Data? Comparing Three Approaches in Developing Research Aspect Classifiers for Biomedical Papers](https://mlsanity.com/paper/2306.04820/) (2023)
- [Low-Resource Multi-Granularity Academic Function Recognition Based on Multiple Prompt Knowledge](https://mlsanity.com/paper/2305.03287/) (2023)
- [Speculative Exploration on the Concept of Artificial Agents Conducting Autonomous Research](https://mlsanity.com/paper/2312.03497/) (2023)
- [GAPMAP: Mapping Scientific Knowledge Gaps in Biomedical Literature Using Large Language Models](https://mlsanity.com/paper/2510.25055/) (2025)

## Abstract (arXiv)

Keeping track of scientific challenges, advances and emerging directions is a fundamental part of research. However, researchers face a flood of papers that hinders discovery of important knowledge. In biomedicine, this directly impacts human lives. To address this problem, we present a novel task of extraction and search of scientific challenges and directions, to facilitate rapid knowledge discovery. We construct and release an expert-annotated corpus of texts sampled from full-length papers, labeled with novel semantic categories that generalize across many types of challenges and directions. We focus on a large corpus of interdisciplinary work relating to the COVID-19 pandemic, ranging from biomedicine to areas such as AI and economics. We apply a model trained on our data to identify challenges and directions across the corpus and build a dedicated search engine. In experiments with 19 researchers and clinicians using our system, we outperform a popular scientific search engine in assisting knowledge discovery. Finally, we show that models trained on our resource generalize to the wider biomedical domain and to AI papers, highlighting its broad utility. We make our data, model and search engine publicly available. https://challenges.apps.allenai.org/