Abstract Automatic Text Summarization (ATS) is an important area in Natural Language
Processing (NLP) with the goal of shortening a long text into a more compact version by …

Contemporary works on abstractive text summarization have focused primarily on high-
resource languages like English, mostly due to the limited availability of datasets for low/mid …

Abstractive text summarization is a task in natural language processing that automatically
generates the summary from the source document in a human-written form with minimal loss …

Dealing with vast amounts of textual data requires the use of efficient systems. Automatic
summarization systems are capable of addressing this issue. Therefore, it becomes highly …

The goal of the cross-lingual summarization (CLS) is to convert a document in one language
(eg, English) to a summary in another one (eg, Chinese). Essentially, the CLS task is the …

To adapt text summarization to the multilingual world, previous work proposes multi-lingual
summarization (MLS) and cross-lingual summarization (CLS). However, these two tasks …

Multimodal abstractive summarization (MAS) aims to produce a concise summary given the
multimodal data (text and vision). Existing studies mainly focus on how to effectively use the …

Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the
performance of large language models (LLMs) on the downstream task. However, there are …

We present CrossSum, a large-scale cross-lingual summarization dataset comprising 1.68
million article-summary samples in 1,500+ language pairs. We create CrossSum by aligning …

Multilingual text summarization requires the ability to understand documents in multiple
languages and generate summaries in the corresponding language, which poses more …