# Attributed Rhetorical Structure Grammar for Domain Text Summarization

arXiv: https://arxiv.org/abs/1909.00923 · HTML: https://mlsanity.com/paper/1909.00923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Sep 2019.

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

- [Sequentially Controlled Text Generation](https://mlsanity.com/paper/2301.02299/) (2023)
- [Multitask Learning for Class-Imbalanced Discourse Classification](https://mlsanity.com/paper/2101.00389/) (2021)
- [Who speaks like a style of Vitamin: Towards Syntax-Aware DialogueSummarization using Multi-task Learning](https://mlsanity.com/paper/2109.14199/) (2021)

## Abstract (arXiv)

This paper presents a new approach of automatic text summarization which combines domain oriented text analysis (DoTA) and rhetorical structure theory (RST) in a grammar form: the attributed rhetorical structure grammar (ARSG), where the non-terminal symbols are domain keywords, called domain relations, while the rhetorical relations serve as attributes. We developed machine learning algorithms for learning such a grammar from a corpus of sample domain texts, as well as parsing algorithms for the learned grammar, together with adjustable text summarization algorithms for generating domain specific summaries. Our practical experiments have shown that with support of domain knowledge the drawback of missing very large training data set can be effectively compensated. We have also shown that the knowledge based approach may be made more powerful by introducing grammar parsing and RST as inference engine. For checking the feasibility of model transfer, we introduced a technique for mapping a grammar from one domain to others with acceptable cost. We have also made a comprehensive comparison of our approach with some others.