Data augmentation has recently seen increased interest in NLP due to more work in low-
resource domains, new tasks, and the popularity of large-scale neural networks that require …

Hallucinations pose a significant challenge to the reliability of neural models for abstractive
summarisation. While automatically generated summaries may be fluent, they often lack …

Insufficient data is one of the main drawbacks in natural language processing tasks, and the
most prevalent solution is to collect a decent amount of data that will be enough for the …

In this paper, an abstractive Arabic text summarization model that is based on sequence-to-
sequence recurrent neural networks is proposed. It consists of a multilayer encoder and …

Every hour, huge amounts of visual contents are posted on social media and user-
generated content platforms. To find relevant videos by means of a natural language query …

In this paper, we propose FFCI, a framework for fine-grained summarization evaluation that
comprises four elements: faithfulness (degree of factual consistency with the source), focus …

Abstractive summarization is the task of compressing a long document into a coherent short
document while retaining salient information. Modern abstractive summarization methods …

Generative transformer-based models have achieved state-of-the-art performance in text
summarization. Nevertheless, they still struggle in real-world scenarios with long documents …

Abstract Development of effective automatic summarization approaches for legal documents
suffer from several challenges like extremely long document-summary pairs, lack of large …

Abstractive summarization systems based on pretrained language models often generate
coherent but factually inconsistent sentences. In this paper, we present a counterfactual data …