# FAST2: an Intelligent Assistant for Finding Relevant Papers

arXiv: https://arxiv.org/abs/1705.05420 · HTML: https://mlsanity.com/paper/1705.05420/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 May 2017.

## Built on by (most cited first, 6 of 6)

- [Artificial Intelligence for Literature Reviews: Opportunities and Challenges](https://mlsanity.com/paper/2402.08565/) (2024)
- [What are the attackers doing now? Automating cyber threat intelligence extraction from text on pace with the changing threat landscape: A survey](https://mlsanity.com/paper/2109.06808/) (2021)
- [Artificial intelligence to automate the systematic review of scientific literature](https://mlsanity.com/paper/2401.10917/) (2024)
- [Automatic selection of primary studies in systematic reviews with evolutionary rule-based classification](https://mlsanity.com/paper/2509.23981/) (2025)
- [Computational Measurement of Political Positions: A Review of Text-Based Ideal Point Estimation Algorithms](https://mlsanity.com/paper/2511.13238/) (2025)
- [NeuroLit Navigator: A Neurosymbolic Approach to Scholarly Article Searches for Systematic Reviews](https://mlsanity.com/paper/2503.00278/) (2025)

## Abstract (arXiv)

Literature reviews are essential for any researcher trying to keep up to date with the burgeoning software engineering literature. FAST2 is a novel tool for reducing the effort required for conducting literature reviews by assisting the researchers to find the next promising paper to read (among a set of unread papers). This paper describes FAST2 and tests it on four large software engineering literature reviews conducted by Wahono (2015), Hall (2012), Radjenovi'c (2013) and Kitchenham (2017). We find that FAST2 is a faster and robust tool to assist researcher finding relevant SE papers which can compensate for the errors made by humans during the review process. The effectiveness of FAST2 can be attributed to three key innovations: (1) a novel way of applying external domain knowledge (a simple two or three keyword search) to guide the initial selection of papers---which helps to find relevant research papers faster with less variances; (2) an estimator of the number of remaining relevant papers yet to be found---which in practical settings can be used to decide if the reviewing process needs to be terminated; (3) a novel self-correcting classification algorithm---automatically corrects itself, in cases where the researcher wrongly classifies a paper.