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Found 8 papers, 0 papers with code
Transformer-based Models for Long Document Summarisation in Financial Domain
Summarisation of long financial documents is a challenging task due to the lack of large-scale datasets and the need for domain knowledge experts to create human-written summaries.
Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications
The advent of Large Language Models (LLMs) heralds a pivotal shift in online user interactions with information.
A Personalized Reinforcement Learning Summarization Service for Learning Structure from Unstructured Data
The exponential growth of textual data has created a crucial need for tools that assist users in extracting meaningful insights.
Towards Personalized and Human-in-the-Loop Document Summarization
We propose novel approaches to tackle these challenges, by: i)enabling automatic intelligent feature engineering, ii) enabling flexible and interactive summarisation, iii) utilising intelligent and personalised summarisation approaches.
A Query Language for Summarizing and Analyzing Business Process Data
In the context of business processes, we consider the Big Data problem as a massive number of interconnected data islands from personal, shared and business data.
Adaptive Summaries: A Personalized Concept-based Summarization Approach by Learning from Users' Feedback
Exploring the tremendous amount of data efficiently to make a decision, similar to answering a complicated question, is challenging with many real-world application scenarios.
Am I Rare? An Intelligent Summarization Approach for Identifying Hidden Anomalies
The experimental results on benchmark datasets prove a summary of the data can be a substitute for original data in the anomaly detection task.
Are We On The Same Page? Hierarchical Explanation Generation for Planning Tasks in Human-Robot Teaming using Reinforcement Learning
In this work, we argue that the agent-generated explanations, especially the complex ones, should be abstracted to be aligned with the level of details the human teammate desires to maintain the recipient's cognitive load.
