| Challenge: | Existing studies use pseudo cross-lingual abstractive summarization data to train neural encoder-decoders. |
| Approach: | They propose a multi-task learning framework for cross-lingual abstractive summarization that attaches a special token to the beginning of the input sentence to indicate the target task. |
| Outcome: | The proposed model achieves better performance than the model trained with only pseudo cross-lingual abstractive summarization data. |
Similar Papers
Cross-Lingual Abstractive Summarization with Limited Parallel Resources (2021.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to cross-lingual summarization use limited available cross-linguistic resources. |
| Approach: | They propose a multi-task framework for cross-lingual abstractive summarization that uses a single decoder to generate monolingual and cross-linguistic summaries. |
| Outcome: | Experiments on two CLS datasets show that the proposed model outperforms baseline models in low-resource and full-dataset scenarios. |
Mixed-Lingual Pre-training for Cross-lingual Summarization (2020.aacl-main)
Copied to clipboard
| Challenge: | Cross-lingual summarization (CLS) aims at producing a summary in the target language for an article in the source language. |
| Approach: | They propose a mixed-lingual pre-training scheme that leverages both cross-lingual tasks such as translation and monolingual tasks like masked language models. |
| Outcome: | The proposed model improves on the translation and masked language models with no task-specific components and saves memory. |
Jointly Learning to Align and Summarize for Neural Cross-Lingual Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Existing studies on cross-lingual summarization focus on pipeline methods and training end-to-end models. |
| Approach: | They propose to jointly learn to align and align to train a neural cross-lingual summarization model by using a large-scale corpus. |
| Outcome: | The proposed model outperforms competing models in most cases and can generate cross-lingual summaries without access to any cross-linguistic corpus. |
NCLS: Neural Cross-Lingual Summarization (D19-1)
Copied to clipboard
| 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. |
CrossSum: Beyond English-Centric Cross-Lingual Summarization for 1,500+ Language Pairs (2023.acl-long)
Copied to clipboard
| Challenge: | a large-scale cross-lingual summarization dataset is available for free . a cross-linguistic summarizing model can be trained in any target language . |
| Approach: | They propose a multistage data sampling algorithm to train a cross-lingual summarization model capable of summarizing an article in any target language. |
| Outcome: | The proposed model outperforms baseline models on ROUGE and LaSE. |
Models and Datasets for Cross-Lingual Summarisation (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent years have witnessed increased interest in abstractive summarisation thanks to the popularity of neural network models and the availability of datasets containing hundreds of thousands of document-summary pairs. |
| Approach: | They propose to create a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in . target language. |
| Outcome: | The proposed task can be applied to several other languages and covers twelve languages and directions. |
A Survey on Cross-Lingual Summarization (2022.tacl-1)
Copied to clipboard
| Challenge: | Cross-lingual summarization is a task of generating a summary in one language for a given document in a different language. |
| Approach: | They present a systematic review of the literature on cross-lingual summarization . they summarize previous efforts and compare them with each other . |
| Outcome: | The proposed approach is compared with previous approaches and summarizes them to provide a deeper analysis. |
A Multi-task Approach to Learning Multilingual Representations (P18-2)
Copied to clipboard
| Challenge: | Using a multi-task model, we learn word and sentence embeddings in a single task. |
| Approach: | They propose a multi-task modeling approach that trains a skip-gram model and a cross-lingual sentence similarity model to learn word and sentence embeddings together. |
| Outcome: | The proposed model can learn word and sentence embeddings in a multilingual distributed representations of text using a cross-lingual sentence similarity model. |
Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for cross-lingual summarization are pipeline-based, but they suffer from error propagation. |
| Approach: | They propose a method that attends to some words in the source text, then translates them into the target language to get the final summary. |
| Outcome: | The proposed method outperforms baseline methods on Chinese-to-English and English-to Chinese summarization tasks. |
Abstractive Multi-Document Summarization via Joint Learning with Single-Document Summarization (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for document summarization are extractive and abstractive. |
| Approach: | They propose to jointly learn an abstractive single-document decoder and a decoding controller to aggregate the decoded outputs for multiple input documents. |
| Outcome: | The proposed model outperforms several baselines on two multi-document summarization datasets and proves that it is useful for both tasks. |