# NLP-Powered Repository and Search Engine for Academic Papers: A Case Study on Cyber Risk Literature with CyLit

arXiv: https://arxiv.org/abs/2409.06226 · HTML: https://mlsanity.com/paper/2409.06226/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 107 references that are in the map.
- First posted on arXiv 10 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [A Survey of the State of Explainable AI for Natural Language Processing](https://mlsanity.com/paper/2010.00711/) (2020)
- [Cyberattack Detection in Mobile Cloud Computing: A Deep Learning Approach](https://mlsanity.com/paper/1712.05914/) (2017)
- [DeepOrigin: End-to-End Deep Learning for Detection of New Malware Families](https://mlsanity.com/paper/1809.08479/) (2018)
- [Cyber Insurance](https://mlsanity.com/paper/1810.00290/) (2018)
- [Post-Incident Audits on Cyber Insurance Discounts](https://mlsanity.com/paper/1908.04867/) (2019)
- [Cyber Risk in Health Facilities: A Systematic Literature Review](https://mlsanity.com/paper/2102.04093/) (2021)
- [A two-sided academic landscape: portrait of highly-cited documents in Google Scholar (1950-2013)](https://mlsanity.com/paper/1607.02861/) (2016)
- [Coverage of highly-cited documents in Google Scholar, Web of Science, and Scopus: a multidisciplinary comparison](https://mlsanity.com/paper/1804.09479/) (2018)
- [A framework for effective corporate communication after cyber security incidents](https://mlsanity.com/paper/2009.09210/) (2020)

## Built on by (most cited first, 3 of 3)

- [InsurTech innovation using natural language processing](https://mlsanity.com/paper/2507.21112/) (2025)
- [Entity-Specific Cyber Risk Assessment using InsurTech Empowered Risk Factors](https://mlsanity.com/paper/2507.08193/) (2025)
- [Claim Automation using Large Language Model](https://mlsanity.com/paper/2602.16836/) (2026)

## Abstract (arXiv)

As the body of academic literature continues to grow, researchers face increasing difficulties in effectively searching for relevant resources. Existing databases and search engines often fall short of providing a comprehensive and contextually relevant collection of academic literature. To address this issue, we propose a novel framework that leverages Natural Language Processing (NLP) techniques. This framework automates the retrieval, summarization, and clustering of academic literature within a specific research domain. To demonstrate the effectiveness of our approach, we introduce CyLit, an NLP-powered repository specifically designed for the cyber risk literature. CyLit empowers researchers by providing access to context-specific resources and enabling the tracking of trends in the dynamic and rapidly evolving field of cyber risk. Through the automatic processing of large volumes of data, our NLP-powered solution significantly enhances the efficiency and specificity of academic literature searches. We compare the literature categorization results of CyLit to those presented in survey papers or generated by ChatGPT, highlighting the distinctive insights this tool provides into cyber risk research literature. Using NLP techniques, we aim to revolutionize the way researchers discover, analyze, and utilize academic resources, ultimately fostering advancements in various domains of knowledge.