Abstractive dialogue summarization generates a concise and fluent summary covering the
salient information in a dialogue among two or more interlocutors. It has attracted significant …

Cross-lingual summarization is the task of generating a summary in one language (eg,
English) for the given document (s) in a different language (eg, Chinese). Under the …

Given a document in a source language, cross-lingual summarization (CLS) aims to
generate a summary in a different target language. Recently, the emergence of Large …

Knowledge editing aims to change language models' performance on several special cases
(ie, editing scope) by infusing the corresponding expected knowledge into them. With the …

Multilingual Large Language Models are capable of using powerful Large Language
Models to handle and respond to queries in multiple languages, which achieves remarkable …

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 …

Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities
in texts that contain visual clues. Rich visual content is valuable for the MMRE task, but …

Cross-Lingual Summarization (CLS) aims at generating summaries in one language for the
given documents in another language. CLS has attracted wide research attention due to its …

Multilingual large language models (MLLMs) leverage advanced large language models to
process and respond to queries across multiple languages, achieving significant success in …