# DEBACER: a method for slicing moderated debates

arXiv: https://arxiv.org/abs/2112.05438 · HTML: https://mlsanity.com/paper/2112.05438/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 10 Dec 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014)
- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [A Network-based End-to-End Trainable Task-oriented Dialogue System](https://mlsanity.com/paper/1604.04562/) (2016)
- [DebateSum: A large-scale argument mining and summarization dataset](https://mlsanity.com/paper/2011.07251/) (2020)
- [A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking](https://mlsanity.com/paper/2006.01554/) (2020)

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

- [ZeroBERTo: Leveraging Zero-Shot Text Classification by Topic Modeling](https://mlsanity.com/paper/2201.01337/) (2022)
- [From Random to Informed Data Selection: A Diversity-Based Approach to Optimize Human Annotation and Few-Shot Learning](https://mlsanity.com/paper/2401.13229/) (2024)
- [No Argument Left Behind: Overlapping Chunks for Faster Processing of Arbitrarily Long Legal Texts](https://mlsanity.com/paper/2410.19184/) (2024)
- [Tracking environmental policy changes in the Brazilian Federal Official Gazette](https://mlsanity.com/paper/2202.10221/) (2022)
- [Science and engineering for what? A large-scale analysis of students' projects in science fairs](https://mlsanity.com/paper/2308.02962/) (2023)
- [Enriching GNNs with Text Contextual Representations for Detecting Disinformation Campaigns on Social Media](https://mlsanity.com/paper/2410.19193/) (2024)

## Abstract (arXiv)

Subjects change frequently in moderated debates with several participants, such as in parliamentary sessions, electoral debates, and trials. Partitioning a debate into blocks with the same subject is essential for understanding. Often a moderator is responsible for defining when a new block begins so that the task of automatically partitioning a moderated debate can focus solely on the moderator's behavior. In this paper, we (i) propose a new algorithm, DEBACER, which partitions moderated debates; (ii) carry out a comparative study between conventional and BERTimbau pipelines; and (iii) validate DEBACER applying it to the minutes of the Assembly of the Republic of Portugal. Our results show the effectiveness of DEBACER. Keywords: Natural Language Processing, Political Documents, Spoken Text Processing, Speech Split, Dialogue Partitioning.