# Summarising Historical Text in Modern Languages

arXiv: https://arxiv.org/abs/2101.10759 · HTML: https://mlsanity.com/paper/2101.10759/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 55 references that are in the map.
- First posted on arXiv 26 Jan 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Get To The Point: Summarization with Pointer-Generator Networks](https://mlsanity.com/paper/1704.04368/) (2017)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [CLUE: A Chinese Language Understanding Evaluation Benchmark](https://mlsanity.com/paper/2004.05986/) (2020)
- [A robust self-learning method for fully unsupervised cross-lingual mappings of word embeddings](https://mlsanity.com/paper/1805.06297/) (2018)
- [A Survey Of Cross-lingual Word Embedding Models](https://mlsanity.com/paper/1706.04902/) (2017)
- [MLSUM: The Multilingual Summarization Corpus](https://mlsanity.com/paper/2004.14900/) (2020)
- [LCSTS: A Large Scale Chinese Short Text Summarization Dataset](https://mlsanity.com/paper/1506.05865/) (2015)
- [NCLS: Neural Cross-Lingual Summarization](https://mlsanity.com/paper/1909.00156/) (2019)
- [Analogical Reasoning on Chinese Morphological and Semantic Relations](https://mlsanity.com/paper/1805.06504/) (2018)
- [Evaluating Word Embedding Models: Methods and Experimental Results](https://mlsanity.com/paper/1901.09785/) (2019)
- [When and Why is Unsupervised Neural Machine Translation Useless?](https://mlsanity.com/paper/2004.10581/) (2020)
- [A Large-Scale Comparison of Historical Text Normalization Systems](https://mlsanity.com/paper/1904.02036/) (2019)
- [Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora](https://mlsanity.com/paper/1606.02820/) (2016)
- [Attention Optimization for Abstractive Document Summarization](https://mlsanity.com/paper/1910.11491/) (2019)

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

- [FactKB: Generalizable Factuality Evaluation using Language Models Enhanced with Factual Knowledge](https://mlsanity.com/paper/2305.08281/) (2023)
- [SikuGPT: A Generative Pre-trained Model for Intelligent Information Processing of Ancient Texts from the Perspective of Digital Humanities](https://mlsanity.com/paper/2304.07778/) (2023)
- [Cross-lingual Cross-temporal Summarization: Dataset, Models, Evaluation](https://mlsanity.com/paper/2306.12916/) (2023)
- [Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation](https://mlsanity.com/paper/2104.04916/) (2021)
- [ClueGraphSum: Let Key Clues Guide the Cross-Lingual Abstractive Summarization](https://mlsanity.com/paper/2203.02797/) (2022)
- [Isomorphic Cross-lingual Embeddings for Low-Resource Languages](https://mlsanity.com/paper/2203.14632/) (2022)

## Abstract (arXiv)

We introduce the task of historical text summarisation, where documents in historical forms of a language are summarised in the corresponding modern language. This is a fundamentally important routine to historians and digital humanities researchers but has never been automated. We compile a high-quality gold-standard text summarisation dataset, which consists of historical German and Chinese news from hundreds of years ago summarised in modern German or Chinese. Based on cross-lingual transfer learning techniques, we propose a summarisation model that can be trained even with no cross-lingual (historical to modern) parallel data, and further benchmark it against state-of-the-art algorithms. We report automatic and human evaluations that distinguish the historic to modern language summarisation task from standard cross-lingual summarisation (i.e., modern to modern language), highlight the distinctness and value of our dataset, and demonstrate that our transfer learning approach outperforms standard cross-lingual benchmarks on this task.