The volume of text data has grown drastically since the internet's inception, which has had a
profound impact on how data is gathered, analyzed, and understood. To address this issues …

Document-level machine translation faces the challenge of data sparsity due to its long input
length and a small amount of training data, increasing the risk of learning spurious patterns …

Human experts write summaries using different techniques, including extracting a sentence
from the document and rewriting it, or fusing various information from the document to …

Multilingual multimodal (MM) summarization, involving the processing of multimodal input
(MI) data across multiple languages to generate corresponding multimodal summaries (MS) …

Medical reports generation from patient–doctor conversations aims to capture the salient
contents from dialogues in the form of medical reports, to assist the healthcare archiving and …

Existing Natural Language to SQL (NL2SQL) solutions have made significant
advancements, yet challenges persist in interpreting and translating NL queries, primarily …

Human experts write summaries using different techniques, including extracting a sentence
from the document and rewriting it, or fusing various information from the document to …

Text summarization is a valuable method for extracting important details from large volumes
of text data, facilitating tasks like text data analysis. Various text summarization techniques …

Abstract Machine learning has emerged as a critical tool in enhancing students' academic
success, particularly in the context of summarizing comprehensive notes and bolstering their …

Despite the striking advances in recent language generation performance, model-generated
responses have suffered from the chronic problem of hallucinations that are either untrue or …