Papers with monolingual summarization
Global Voices: Crossing Borders in Automatic News Summarization (D19-54)
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| Challenge: | a crowd-sourced dataset is needed to evaluate cross-lingual summarization methods . human-written summarizing is expensive and difficult to design for humans . |
| Approach: | They construct a multilingual dataset for evaluating cross-lingual summarization methods . they use social-network descriptions of news articles to extract evaluation data . |
| Outcome: | The proposed dataset compares a translate-then-summarize approach with baselines in 15 languages. |
Language-Independent Representations Improve Zero-Shot Summarization (2024.naacl-short)
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| Challenge: | Pretrained models can be fine tuned on downstream generation tasks, but they can fail in zero-shot conditions. |
| Approach: | They propose query-key finetuning to decouple task-specific knowledge from pretrained models . they propose a variant that more directly enforces language-agnostic representations . |
| Outcome: | The proposed model decouples task-specific knowledge from pretrained language generation abilities. |
SumTra: A Differentiable Pipeline for Few-Shot Cross-Lingual Summarization (2024.naacl-long)
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| Challenge: | Existing approaches to cross-lingual summarization are limited due to limited training data. |
| Approach: | They propose to re-use existing multilingual summarization and translation pipelines to perform cross-lingual summaries in a sequence. |
| Outcome: | The proposed approach outperforms existing methods in many languages with only 10% of the fine-tuning samples. |
A Variational Hierarchical Model for Neural Cross-Lingual Summarization (2022.acl-long)
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| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods or jointly training an end-to-end model through an auxiliary MT or MS objective. |
| Approach: | They propose a hierarchical model for the cross-lingual summarization task . the model is based on the conditional variational auto-encoder . |
| Outcome: | The proposed model generates better cross-lingual summaries than comparison models in the few-shot setting. |
Understanding Translationese in Cross-Lingual Summarization (2023.findings-emnlp)
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| Challenge: | Existing datasets involve translation, but translationese is distinguished from original text . previous studies have shown that translationeses in CLS are not a problem in training sets . |
| Approach: | They propose to use cross-lingual summarization to generate a concise summary in a target language from a document in . existing datasets typically involve translation in their creation, but the translated text is distinguished from the original written in that language. |
| Outcome: | The proposed method systematically investigates how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. |
NCLS: Neural Cross-Lingual Summarization (D19-1)
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| Challenge: | Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation. |
| Approach: | They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization. |
| Outcome: | The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets. |
Evaluating Factuality in Cross-lingual Summarization (2023.findings-acl)
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| Challenge: | Existing evaluation metrics for monolingual summarization require translation to evaluate the factuality of cross-lingual summmarization. |
| Approach: | They propose to analyze cross-lingual factuality by collecting annotations and generated summaries from models at summary level and sentence level. |
| Outcome: | The proposed dataset shows that over 50% of generated summaries contain factual errors with different characteristics from monolingual summarization. |