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 …

The automatic generation of a text summary is a task of generating a short summary for a
relatively long text document by capturing its key information. In the past, supervised …

Event relation extraction (ERE) is a critical and fundamental challenge for natural language
processing. Existing work mainly focuses on directly modeling the entire document, which …

Long sentences have been a persistent issue in written communication for many years since
they make it challenging for readers to grasp the main points or follow the initial intention of …

Cross-Lingual Summarization (CLS) is the task to generate a summary in one language for
an article in a different language. Previous studies on CLS mainly take pipeline methods or …

Prior work has shown that coupling sequential latent variable models with semantic
ontological knowledge can improve the representational capabilities of event modeling …

As an essential task in the field of knowledge graph, relation extraction (RE) has received
extensive attention from researchers. Since the existing RE methods only adopt one trained …

Abstractive text summarization aims to capture important information from text and integrate
contextual information to guide the summary generation. However, effective integration of …

Frame-semantic Parsing (FSP) is a challenging and critical task in Natural Language
Processing (NLP). Most of the existing studies decompose the FSP task into frame …

Text semantic similarity computation is a fundamental problem in the field of natural
language processing. In recent years, text semantic similarity algorithms based on deep …