# The Cross-lingual Conversation Summarization Challenge

arXiv: https://arxiv.org/abs/2205.00379 · HTML: https://mlsanity.com/paper/2205.00379/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 30 references that are in the map.
- First posted on arXiv 1 May 2022.

## Builds on (most cited first, 20 of 20)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [Neural Text Summarization: A Critical Evaluation](https://mlsanity.com/paper/1908.08960/) (2019)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2010.03093/) (2020)
- [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://mlsanity.com/paper/2103.06410/) (2021)
- [DialogLM: Pre-trained Model for Long Dialogue Understanding and Summarization](https://mlsanity.com/paper/2109.02492/) (2021)
- [NCLS: Neural Cross-Lingual Summarization](https://mlsanity.com/paper/1909.00156/) (2019)
- [A Survey on Dialogue Summarization: Recent Advances and New Frontiers](https://mlsanity.com/paper/2107.03175/) (2021)
- [Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization](https://mlsanity.com/paper/2105.12544/) (2021)
- [Models and Datasets for Cross-Lingual Summarisation](https://mlsanity.com/paper/2202.09583/) (2022)
- [Cross-Lingual Abstractive Summarization with Limited Parallel Resources](https://mlsanity.com/paper/2105.13648/) (2021)
- [ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization](https://mlsanity.com/paper/2202.05599/) (2022)
- [Mixed-Lingual Pre-training for Cross-lingual Summarization](https://mlsanity.com/paper/2010.08892/) (2020)
- [A Variational Hierarchical Model for Neural Cross-Lingual Summarization](https://mlsanity.com/paper/2203.03820/) (2022)
- [Autocorrect in the Process of Translation -- Multi-task Learning Improves Dialogue Machine Translation](https://mlsanity.com/paper/2103.16189/) (2021)
- [SummerTime: Text Summarization Toolkit for Non-experts](https://mlsanity.com/paper/2108.12738/) (2021)

## Built on by (most cited first, 8 of 8)

- [UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization](https://mlsanity.com/paper/2211.09783/) (2022)
- [Towards Unifying Multi-Lingual and Cross-Lingual Summarization](https://mlsanity.com/paper/2305.09220/) (2023)
- [Understanding Translationese in Cross-Lingual Summarization](https://mlsanity.com/paper/2212.07220/) (2022)
- [Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation](https://mlsanity.com/paper/2307.04018/) (2023)
- [Semantic-based Pre-training for Dialogue Understanding](https://mlsanity.com/paper/2209.09146/) (2022)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [GOAL: Towards Benchmarking Few-Shot Sports Game Summarization](https://mlsanity.com/paper/2207.08635/) (2022)
- [Cross-domain Generalization for AMR Parsing](https://mlsanity.com/paper/2210.12445/) (2022)

## Abstract (arXiv)

We propose the shared task of cross-lingual conversation summarization, ConvSumX Challenge, opening new avenues for researchers to investigate solutions that integrate conversation summarization and machine translation. This task can be particularly useful due to the emergence of online meetings and conferences. We construct a new benchmark, covering 2 real-world scenarios and 3 language directions, including a low-resource language. We hope that ConvSumX can motivate researches to go beyond English and break the barrier for non-English speakers to benefit from recent advances of conversation summarization.