# Shaping Political Discourse using multi-source News Summarization

arXiv: https://arxiv.org/abs/2312.11703 · HTML: https://mlsanity.com/paper/2312.11703/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 12 of 12)

- [Text Summarization with Pretrained Encoders](https://mlsanity.com/paper/1908.08345/) (2019)
- [Generating Wikipedia by Summarizing Long Sequences](https://mlsanity.com/paper/1801.10198/) (2018)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [Graph-based Neural Multi-Document Summarization](https://mlsanity.com/paper/1706.06681/) (2017)
- [Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization](https://mlsanity.com/paper/1808.06218/) (2018)
- [Using Local Knowledge Graph Construction to Scale Seq2Seq Models to Multi-Document Inputs](https://mlsanity.com/paper/1910.08435/) (2019)
- [Footwear Size Recommendation System](https://mlsanity.com/paper/1806.11423/) (2018)
- [One Embedding To Do Them All](https://mlsanity.com/paper/1906.12120/) (2019)
- [Classification for everyone : Building geography agnostic models for fairer recognition](https://mlsanity.com/paper/2312.02957/) (2023)
- [Exploring Graph Based Approaches for Author Name Disambiguation](https://mlsanity.com/paper/2312.08388/) (2023)

## Built on by (most cited first, 5 of 5)

- [Summarization of Opinionated Political Documents with Varied Perspectives](https://mlsanity.com/paper/2411.04093/) (2024)
- [When Bigger Isn't Better: A Comprehensive Fairness Evaluation of Political Bias in Multi-News Summarisation](https://mlsanity.com/paper/2604.21309/) (2026)
- [Zero-shot Active Learning Using Self Supervised Learning](https://mlsanity.com/paper/2401.01690/) (2024)
- [Detecting anxiety from short clips of free-form speech](https://mlsanity.com/paper/2312.15272/) (2023)
- [Exploring intra-task relations to improve meta-learning algorithms](https://mlsanity.com/paper/2312.16612/) (2023)

## Abstract (arXiv)

Multi-document summarization is the process of automatically generating a concise summary of multiple documents related to the same topic. This summary can help users quickly understand the key information from a large collection of documents. Multi-document summarization systems are more complex than single-document summarization systems due to the need to identify and combine information from multiple sources. In this paper, we have developed a machine learning model that generates a concise summary of a topic from multiple news documents. The model is designed to be unbiased by sampling its input equally from all the different aspects of the topic, even if the majority of the news sources lean one way.