LR-Sum: Summarization for Less-Resourced Languages
This preprint describes work in progress on LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages. LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022). The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets. We describe how we plan to use the data for modeling experiments and discuss limitations of the dataset.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Comparing Approaches to Automatic Summarization in Less-Resourced Languages
Automatic text summarization has achieved high performance in high-resourced languages like English, but comparatively less attention has been given to summarization in less-resourced languages. This work compares a vari…
Text SummarizationData AugmentationQFS-Composer: Query-focused summarization pipeline for less resourced languages
Large language models (LLMs) demonstrate strong performance in text summarization, yet their effectiveness drops significantly across languages with restricted training resources. This work addresses the challenge of que…
Question GenerationText SummarizationQuestion AnsweringUnsupervised Approach to Multilingual User Comments Summarization
User commenting is a valuable feature of many news outlets, enabling them a contact with readers and enabling readers to express their opinion, provide different viewpoints, and even complementary information. Yet, large…
Extractive SummarizationSentenceSequence to sequence pretraining for a less-resourced Slovenian language
Large pretrained language models have recently conquered the area of natural language processing. As an alternative to predominant masked language modelling introduced in BERT, the T5 model has introduced a more general …
Language ModelingLanguage ModellingMachine TranslationOpen-Domain Question Answering+4Open or Closed LLM for Lesser-Resourced Languages? Lessons from Greek
Natural Language Processing (NLP) for lesser-resourced languages faces persistent challenges, including limited datasets, inherited biases from high-resource languages, and the need for domain-specific solutions. This st…
Authorship Attribution