Compressive Summarisation

Compressive summarization aims to create concise summaries that retain key information, addressing limitations of both extractive and abstractive methods. Recent research focuses on developing unsupervised learning approaches, particularly using reinforcement learning with dual-agent architectures (e.g., extractor and compressor agents) to optimize for both semantic coverage and fluency without relying on large, manually-labeled datasets. This work is expanding into cross-lingual summarization, leveraging diverse data sources like news articles and videos to build multilingual models. The development of effective and efficient compressive summarization techniques has significant implications for information retrieval, text mining, and cross-lingual communication.