Evaluating the effectiveness of large language models in meeting summarization with transcript segmentation techniques: How well does gpt-3.5-turbo perform on meeting summarization with topic and context-length window segmentation?

Large Language Models (LLM) have brought significant performance increase on many Natural Language Processing tasks. However LLMs have not been tested for meeting summarization. This research paper examines the effectiveness of the gpt-3.5-turbo model on the meeting summarization domain. However due to input length limitations, it cannot be...

Zero-shot learning for (dis)agreement detection in meeting trancripts: Comparing latent topic models and large language models

This paper presents a novel approach to detect agreement and disagreement moments between participants in meeting transcripts without relying on labeled data. We propose a model in which disagreement detection is defined as the process of first identifying argumentative theses relevant to a given corpus of text and then classifying all phrases...

ConceptEVA: Concept-Based Interactive Exploration and Customization of Document Summaries

With the most advanced natural language processing and artificial intelligence approaches, effective summarization of long and multi-topic documents - such as academic papers - for readers from different domains still remains a challenge. To address this, we introduce ConceptEVA, a mixed-initiative approach to generate, evaluate, and...

Extending Source Code Pre-Trained Language Models to Summarise Decompiled Binaries

Binary reverse engineering is used to understand and analyse programs for which the source code is unavailable. Decompilers can help, transforming opaque binaries into a more readable source code-like representation. Still, reverse engineering is difficult and costly, involving considering effort in labelling code with helpful summaries....

Image-based Video Search Engine: Keyframe Extraction

In this report, the analysis and design of a system that extracts keyframes from videos is detailed. The need for such a sub-module stems from the similarity of frames in a video. To aid in reducing the computation time of the content based video search engine, the Keyframe Extraction Module reduces the amount of frames by discarding frames that...

SuMe: A Dataset Towards Summarizing Biomedical Mechanisms

Can language models read biomedical texts and explain the biomedical mechanisms discussed? In this work we introduce a biomedical mechanism summarization task. Biomedical studies often investigate the mechanisms behind how one entity (e.g., a protein or a chemical) affects another in a biological context. The abstracts of these publications...

Evaluation of Video Summarization Using Fully Convolutional Sequence Networks on Action Localization Datasets

In the problem of video summarization, the goal is to select a subset of the input frames conveying the most important information of the input video. The collection of data proves to be a challenging task. In part because there exists a disagreement among human annotators on what segments of a video should be considered important for a summary....

Evaluating the Supervised Video Summarization Model VASNet on an Action Localization Dataset

There is growing research on automated video summarization following the rise of video content. However, the subjectivity of the task itself is still an issue to address. This subjectivity stems from the fact that there can be different summaries for the same video depending on which parts one considers important. Supervised models especially...

Evaluation of Video Summarization using DSNet and Action Localization Datasets

In this paper, the DSNet framework used for automatic video summarization gets reviewed when using action localization datasets. The problem facing video summarizations using deep learning techniques is that datasets can be subjective depending on preferences of human annotators, making for noise in the labeling. This paper will look at a anchor...

Evaluation of the SUM-GAN-AAE method for Video Summarization

Video summarization is a task which many researchers have tried to automate with deep learning methods. One of these methods is the SUM-GAN-AAE algorithm developed by Apostolidis et al. which is an unsupervised machine learning method evaluated in this study. The research aims at testing the algorithm's performance on the Breakfast dataset,...

Choosing from skyline sets

The skyline operator has been proposed to bridge the gap between traditional and multimedia database systems by finding the optimal objects according to the notion of Pareto dominance. According to the notion of Pareto dominance an object dominates another if it is better in one attribute and equal in all others. Skyline sets end up being pretty...

The Impact of Test Case Summaries on Bug Fixing Performance: An Empirical Investigation

Automated test generation tools have been widely investigated with the goal of reducing the cost of testing activities. However, generated tests have been shown not to help developers in detecting and finding more bugs even though they reach higher structural coverage compared to manual testing. The main reason is that generated tests are...

Advancing the Relevance Criteria for Video Search and Visual Summarization

To facilitate finding of relevant information in ever-growing multimedia collections, a number of multimedia information retrieval solutions have been proposed over the past years. The essential element of any such solution is the relevance criterion deployed to select or rank the items from a multimedia collection to be presented to the user....

Automatic Creation of Opinion-Based Summary Representations

Customers interested in buying a product, can search on the internet for reviews about that product. For many products, an enormous amount of information and opinions is available. Customers gets overwhelmed by this information and systems are needed to filter out the essential information. In this research, a model is developed to automatically...

Multimedia Content Analysis, Indexing and Summarization: A Perspective on Real-Life Use Cases

The problem of finding images, video clips and music, given time, place, interest and mood has kept an immense number of scientists and technology developers busy in the past twenty years. However, straight forward attempts to apply text based search to non-textual data still seem to be the only viable solution. In spite of the numerous ideas...