Auto-hMDS: Automatic Construction of a Large Heterogeneous Multilingual Multi-Document Summarization Corpus (L18-1)
Copied to clipboard
| Challenge: | Existing datasets for automatic text summarization are small and focused on newswires. |
| Approach: | They propose to automatically generate a large multilingual multi-document summarization corpus using Wikipedia articles as summaries and to automatically search for appropriate source documents. |
| Outcome: | The proposed corpus contains 7,316 topics in English and German with different summary lengths and number of source documents. |
Similar Papers
Beyond Generic Summarization: A Multi-faceted Hierarchical Summarization Corpus of Large Heterogeneous Data (L18-1)
Copied to clipboard
| Challenge: | Automated summarization has focused on ten to twenty documents, typically news articles, but could in theory analyze hundreds of documents from a wide range of sources and provide an overview to the interested reader. |
| Approach: | They propose a method for creating hierarchical summarization corpora from large, heterogeneous document collections by crowdsourcing relevant content and asking trained annotators to order the relevant information hierarchically. |
| Outcome: | The proposed method can be used to develop and evaluate hierarchical summarization systems. |
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. |
Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model (P19-1)
Copied to clipboard
| Challenge: | Multi-document summarization (MDS) of news articles has been limited to datasets of a couple of hundred examples. |
| Approach: | They propose a model which integrates a traditional extractive summarization model with a standard SDS model and achieves competitive results on MDS datasets. |
| Outcome: | The proposed model achieves competitive results on large-scale datasets. |
MassiveSumm: a very large-scale, very multilingual, news summarisation dataset (2021.emnlp-main)
Copied to clipboard
| Challenge: | Current research in automatic summarisation is expensive to create, posing a challenge for any language. |
| Approach: | They propose to use a large-scale multilingual summarisation dataset with articles in 92 languages and more than 35 writing scripts to generate a multilingual dataset. |
| Outcome: | The proposed method is the largest, most inclusive, existing dataset and one of the largest and most inclusive datasets for any NLP task. |
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal (2020.acl-main)
Copied to clipboard
| Challenge: | Multidocument summarization (MDS) aims to compress large document collections into short summaries. |
| Approach: | They propose a large-scale multidocument summarization dataset that is large both in total number of document clusters and in the size of individual clusters. |
| Outcome: | The proposed dataset is large both in the total number of document clusters and in the size of individual clusters. |
Summarization Beyond News: The Automatically Acquired Fandom Corpora (2020.lrec-1)
Copied to clipboard
| Challenge: | Abstractive summarization methods require large corpora to train neural architectures. |
| Approach: | They propose a novel automatic corpus construction approach that automatically constructs large open-licensed summarization corpora from existing large text collections and an evaluation process with human annotators. |
| Outcome: | The proposed approach can be used to train abstractive summarization models on large corpora and through a manual evaluation with human annotators. |
MLSUM: The Multilingual Summarization Corpus (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing biases in multi-lingual datasets are limiting the use of multilingual data in document summarization tasks. |
| Approach: | They present MLSUM, the first large-scale MultiLingual SUMmarization dataset. |
| Outcome: | The proposed dataset contains 1.5M+ article/summary pairs in five different languages. |
DaNewsroom: A Large-scale Danish Summarisation Dataset (2020.lrec-1)
Copied to clipboard
| Challenge: | Existing datasets for automatic summarisation are English-oriented . however, only very limited datasets exist in languages other than English . |
| Approach: | They present the first large-scale non-English dataset specifically curated for automatic summarisation. |
| Outcome: | The proposed dataset is the first for the Danish language and is compared with existing datasets. |
Topic-Guided Abstractive Multi-Document Summarization (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing studies on multi-document summarization (MDS) focus on extractive and abstractive approaches to create a fluent and concise summary for a collection of thematically related documents. |
| Approach: | They propose a novel abstractive MDS model that represents multiple documents as a heterogeneous graph and then applies a graph-to-sequence framework to generate summaries. |
| Outcome: | The proposed model outperforms state-of-the-art models on Rouge scores and human evaluation, while learning high-quality topics. |
Towards Multi-dimensional Evaluation of LLM Summarization across Domains and Languages (2025.acl-long)
Copied to clipboard
Hyangsuk Min, Yuho Lee, Minjeong Ban, Jiaqi Deng, Nicole Hee-Yeon Kim, Taewon Yun, Hang Su, Jason Cai, Hwanjun Song
| Challenge: | Existing evaluation frameworks for text summarization lack domain-specific assessment criteria and are predominantly English-centric. |
| Approach: | They propose a multi-dimensional, multi-domain evaluation of summarization in English and Chinese that incorporates specialized assessment criteria for each domain and leverages a debate system to enhance annotation quality. |
| Outcome: | The proposed evaluation framework provides a multi-dimensional, multi-domain evaluation of summarization in English and Chinese. |