Document Summarization

Document summarization aims to automatically condense large text documents into concise summaries retaining key information. Current research emphasizes improving the accuracy and relevance of summaries generated by smaller, more accessible language models, often through techniques like instruction tuning and incorporating key element identification. This field is crucial for managing information overload across diverse applications, from news aggregation and legal case retrieval to medical record analysis and assisting users in identifying phishing emails. Ongoing efforts focus on handling diverse data types (including tables and images), addressing issues like hallucination and bias in large language models, and developing robust evaluation metrics.