# RTSUM: Relation Triple-based Interpretable Summarization with Multi-level Salience Visualization

arXiv: https://arxiv.org/abs/2310.13895 · HTML: https://mlsanity.com/paper/2310.13895/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 21 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://mlsanity.com/paper/1912.08777/) (2019)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents](https://mlsanity.com/paper/1804.05685/) (2018)
- [Skip-Thought Vectors](https://mlsanity.com/paper/1506.06726/) (2015)
- [Sentence Centrality Revisited for Unsupervised Summarization](https://mlsanity.com/paper/1906.03508/) (2019)
- [Understanding Neural Abstractive Summarization Models via Uncertainty](https://mlsanity.com/paper/2010.07882/) (2020)
- [TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising](https://mlsanity.com/paper/2001.00725/) (2020)
- [Variations of the Similarity Function of TextRank for Automated Summarization](https://mlsanity.com/paper/1602.03606/) (2016)
- [SEQ^3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression](https://mlsanity.com/paper/1904.03651/) (2019)
- [How Far are We from Robust Long Abstractive Summarization?](https://mlsanity.com/paper/2210.16732/) (2022)
- [Unsupervised Extractive Summarization using Pointwise Mutual Information](https://mlsanity.com/paper/2102.06272/) (2021)
- [Unsupervised Extractive Summarization by Pre-training Hierarchical Transformers](https://mlsanity.com/paper/2010.08242/) (2020)
- [Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks](https://mlsanity.com/paper/1810.02851/) (2018)
- [Leveraging Knowledge in Multilingual Commonsense Reasoning](https://mlsanity.com/paper/2110.08462/) (2021)

## Built on by (most cited first, 1 of 1)

- [LOOMSUM: Weaving Quantitative and Narrative Evidence for Faithful Long Text-Table Summarization](https://mlsanity.com/paper/2609.00241/) (2026)

## Abstract (arXiv)

In this paper, we present RTSUM, an unsupervised summarization framework that utilizes relation triples as the basic unit for summarization. Given an input document, RTSUM first selects salient relation triples via multi-level salience scoring and then generates a concise summary from the selected relation triples by using a text-to-text language model. On the basis of RTSUM, we also develop a web demo for an interpretable summarizing tool, providing fine-grained interpretations with the output summary. With support for customization options, our tool visualizes the salience for textual units at three distinct levels: sentences, relation triples, and phrases. The codes,are publicly available.