# TOPICAL: TOPIC Pages AutomagicaLly

arXiv: https://arxiv.org/abs/2405.01796 · HTML: https://mlsanity.com/paper/2405.01796/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 14 of 14)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [Atlas: Few-shot Learning with Retrieval Augmented Language Models](https://mlsanity.com/paper/2208.03299/) (2022)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [KILT: a Benchmark for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2009.02252/) (2020)
- [PaperQA: Retrieval-Augmented Generative Agent for Scientific Research](https://mlsanity.com/paper/2312.07559/) (2023)
- [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://mlsanity.com/paper/2211.13308/) (2022)
- [Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search](https://mlsanity.com/paper/2203.08436/) (2022)
- [A Computational Inflection for Scientific Discovery](https://mlsanity.com/paper/2205.02007/) (2022)
- [Open Domain Multi-document Summarization: A Comprehensive Study of Model Brittleness under Retrieval](https://mlsanity.com/paper/2212.10526/) (2022)
- [Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery](https://mlsanity.com/paper/2108.05669/) (2021)
- [ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts](https://mlsanity.com/paper/2205.06982/) (2022)
- [High-Precision Extraction of Emerging Concepts from Scientific Literature](https://mlsanity.com/paper/2006.06877/) (2020)
- [Generating Topic Pages for Scientific Concepts Using Scientific Publications](https://mlsanity.com/paper/2304.11922/) (2023)

## Abstract (arXiv)

Topic pages aggregate useful information about an entity or concept into a single succinct and accessible article. Automated creation of topic pages would enable their rapid curation as information resources, providing an alternative to traditional web search. While most prior work has focused on generating topic pages about biographical entities, in this work, we develop a completely automated process to generate high-quality topic pages for scientific entities, with a focus on biomedical concepts. We release TOPICAL, a web app and associated open-source code, comprising a model pipeline combining retrieval, clustering, and prompting, that makes it easy for anyone to generate topic pages for a wide variety of biomedical entities on demand. In a human evaluation of 150 diverse topic pages generated using TOPICAL, we find that the vast majority were considered relevant, accurate, and coherent, with correct supporting citations. We make all code publicly available and host a free-to-use web app at: https://s2-topical.apps.allenai.org