# Generating Topic Pages for Scientific Concepts Using Scientific Publications

arXiv: https://arxiv.org/abs/2304.11922 · HTML: https://mlsanity.com/paper/2304.11922/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 24 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguation](https://mlsanity.com/paper/2010.14678/) (2020)
- [A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency](https://mlsanity.com/paper/1911.01678/) (2019)
- [Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions](https://mlsanity.com/paper/2010.05129/) (2020)
- [ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts](https://mlsanity.com/paper/2205.06982/) (2022)

## Built on by (most cited first, 2 of 2)

- [WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset](https://mlsanity.com/paper/2308.03582/) (2023)
- [TOPICAL: TOPIC Pages AutomagicaLly](https://mlsanity.com/paper/2405.01796/) (2024)

## Abstract (arXiv)

In this paper, we describe Topic Pages, an inventory of scientific concepts and information around them extracted from a large collection of scientific books and journals. The main aim of Topic Pages is to provide all the necessary information to the readers to understand scientific concepts they come across while reading scholarly content in any scientific domain. Topic Pages are a collection of automatically generated information pages using NLP and ML, each corresponding to a scientific concept. Each page contains three pieces of information: a definition, related concepts, and the most relevant snippets, all extracted from scientific peer-reviewed publications. In this paper, we discuss the details of different components to extract each of these elements. The collection of pages in production contains over 360,000 Topic Pages across 20 different scientific domains with an average of 23 million unique visits per month, constituting it a popular source for scientific information.