# Full-Text Argumentation Mining on Scientific Publications

arXiv: https://arxiv.org/abs/2210.13084 · HTML: https://mlsanity.com/paper/2210.13084/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts](https://mlsanity.com/paper/1710.06071/) (2017)
- [SciDTB: Discourse Dependency TreeBank for Scientific Abstracts](https://mlsanity.com/paper/1806.03653/) (2018)
- [Claim Extraction in Biomedical Publications using Deep Discourse Model and Transfer Learning](https://mlsanity.com/paper/1907.00962/) (2019)
- [Scientific Discourse Tagging for Evidence Extraction](https://mlsanity.com/paper/1909.04758/) (2019)
- [Experiment Segmentation in Scientific Discourse as Clause-level Structured Prediction using Recurrent Neural Networks](https://mlsanity.com/paper/1702.05398/) (2017)

## Built on by (most cited first, 4 of 4)

- [API-Assisted Code Generation for Question Answering on Varied Table Structures](https://mlsanity.com/paper/2310.14687/) (2023)
- [On the Role of Context for Discourse Relation Classification in Scientific Writing](https://mlsanity.com/paper/2510.26354/) (2025)
- [ClaimFlow: Tracing the Evolution of Scientific Claims in NLP](https://mlsanity.com/paper/2603.16073/) (2026)
- [TTLab at Daleel 2026: STAR-Ar, Sequence Tagging for Argument Recognition in Arabic](https://mlsanity.com/paper/2609.39385/) (2026)

## Abstract (arXiv)

Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent.