Dialogue summarization aims to condense the original dialogue into a shorter version
covering salient information, which is a crucial way to reduce dialogue data overload …

Specifically focusing on the landscape of abstractive text summarization, as opposed to
extractive techniques, this survey presents a comprehensive overview, delving into state-of …

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 …

Compared to Full-Model Fine-Tuning (FMFT), Parameter Efficient Fine-Tuning (PEFT) has
demonstrated superior performance and lower computational overhead in several code …

We present ClidSum, a benchmark dataset for building cross-lingual summarization systems
on dialogue documents. It consists of 67k+ dialogue documents from two subsets (ie …

Multi-Modal Entity Alignment (MMEA) is a critical task that aims to identify equivalent entity
pairs across multi-modal knowledge graphs (MMKGs). However, this task faces challenges …

Incrementally expanding the capability of an existing translation model to solve new domain
tasks over time is a fundamental and practical problem, which usually suffers from …

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 …

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