# Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation

arXiv: https://arxiv.org/abs/2110.06354 · HTML: https://mlsanity.com/paper/2110.06354/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 49 references that are in the map.
- First posted on arXiv 12 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019)
- [What Should I Learn First: Introducing LectureBank for NLP Education and Prerequisite Chain Learning](https://mlsanity.com/paper/1811.12181/) (2018)
- [Course Concept Expansion in MOOCs with External Knowledge and Interactive Game](https://mlsanity.com/paper/1909.07739/) (2019)
- [TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation](https://mlsanity.com/paper/1805.04617/) (2018)
- [Citation Classification for Behavioral Analysis of a Scientific Field](https://mlsanity.com/paper/1609.00435/) (2016)

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

- [SKG: A Versatile Information Retrieval and Analysis Framework for Academic Papers with Semantic Knowledge Graphs](https://mlsanity.com/paper/2306.04758/) (2023)
- [Adapting Pretrained Language Models for Citation Classification via Self-Supervised Contrastive Learning](https://mlsanity.com/paper/2505.14471/) (2025)
- [ACL-rlg: A Dataset for Reading List Generation](https://mlsanity.com/paper/2502.15692/) (2024)

## Abstract (arXiv)

Recent years have witnessed the dramatic growth of paper volumes with plenty of new research papers published every day, especially in the area of computer science. How to glean papers worth reading from the massive literature to do a quick survey or keep up with the latest advancement about a specific research topic has become a challenging task. Existing academic search engines such as Google Scholar return relevant papers by individually calculating the relevance between each paper and query. However, such systems usually omit the prerequisite chains of a research topic and cannot form a meaningful reading path. In this paper, we introduce a new task named Reading Path Generation (RPG) which aims at automatically producing a path of papers to read for a given query. To serve as a research benchmark, we further propose SurveyBank, a dataset consisting of large quantities of survey papers in the field of computer science as well as their citation relationships. Each survey paper contains key phrases extracted from its title and multi-level reading lists inferred from its references. Furthermore, we propose a graph-optimization-based approach for reading path generation which takes the relationship between papers into account. Extensive evaluations demonstrate that our approach outperforms other baselines. A Real-time Reading Path Generation System (RePaGer) has been also implemented with our designed model. To the best of our knowledge, we are the first to target this important research problem. Our source code of RePaGer system and SurveyBank dataset can be found on here.