Congressional hearings are at the center of legislation, yet their analysis is hindered by the volume and complexity of the transcripts. While recent advances in Natural Language Processing (NLP) have enabled political discourse analysis using automated tools, conventional topic modeling methods often struggle to produce semantically coherent topics due to their reliance on context-free word frequencies. This paper evaluates the performance of a new transformer-based topic modeling technique, focusing on its application to policy discussions through a detailed case study. Two variants of BERTopic are considered: (1) a parameter-tuned model and (2) a zero-shot variant, evaluated on U.S. congressional hearing transcripts from 2021 to 2024. The results demonstrate that the zero-shot version achieves competitive coherence with increased interpretability and stability, making it a useful resource for policymakers and researchers alike. This paper establishes a foundational methodological framework for automated legislative text analysis. It also outlines the trade-offs between unsupervised and semi-supervised topic modeling in political usage.

A well-functioning democracy depends on an informed population. To help informing citizens, summaries of arguments in political transcripts can be made. An approach to argument summarization is the creation of summaries through distillation of the arguments into higher-level key points. In this approach, mapping arguments to key points is an important subtask. This study examines how model selection, prompting strategy, choice of domain, and input batching influence the performance of large language models (LLMs) in matching arguments to key points. We introduce a self-annotated dataset from U.S. Congress committee transcripts and evaluate both generative and embedding-based models on this task. Generative LLMs (GPT-3.5-turbo, o4-mini) outperform both untuned and fine-tuned RoBERTa in zero-shot argument-to-keypoint mapping (up to 0.880 macro-F1), while sparse two-shot prompting yields no gains. Moderate batching (n=32) boosts throughput without losing accuracy. These results show that a fully automated KPA pipeline—argument extraction, key-point generation, and mapping—is achievable with current LLMs.

Meetings represent a key component of collabora- tion in the workplace, serving purposes like brain- storming, discussion, and negotiation. Despite their importance, reaching a consensus among partici- pants can frequently be difficult because different people can leave the debate with different perspec- tives. In order to promote efficient communication and decision-making in organisational contexts, the use of the Shape Language is proposed. The Shape Language consists of shapes that people can use in meetings in order to represent abstract ideas, that would be difficult to represent by only words. In order to track how people interact with these ob- jects, computer vision tools can be used. This study aims to explore the current existing computer vi- sion tools for segmenting and classifying objects in meetings, aiming to find limitations in how well these models are able to recognize objects in the context of meetings and negotiations. Results of this study show that after fine-tuning four models on the custom dataset, they can recognize the three shapes provided as classes in most of the cases, but still make mistakes when assigning classes, or miss objects that they should classify all together, which show limitations of these modern tools.

Within the field of negotiations, a recent publication is a paper called the NegotiAct[9], which analyzed existing coding schemes of negotiations and introduced an improvement on them that promises a viable way to analyze negotiations in depth. In this research, my goal is to develop a workflow for gathering information and analyzing it with the NegotiAct. To this end the Colored Trails Game [7] is used to design an experiment that simulates real-life negotiations. The Colored Trails Game is a game where each player, through negotiating tries to maximize their own score under limited resources. The game at its core offers the opportunity for both cooperation and competitiveness.
In the experiment a total of 15 participants took part and it was run a total of 20 times, resulting in 3 hours and 10 minutes of recordings. Encoding them with the NegotiAct resulted in the discerning of a total of 87 offers made, 24 offers accepted, 26 offers rejected, and 16 requests for offer modifications. Based on this coherent mapping it can be concluded that the Colored Trails Game is a suitable choice for the workflow of gathering data to be put through the analysis of the NegotiAct.

Meetings are a vital part of discussions and negotiations. Unfortunately, individuals often leave with a vague understanding of the topics covered during the meeting and tend to forget even more of what transpired as time goes on. Driven by previous research that attempts to solve the issue by using architectural shapes as a way of removing ambiguity along with recent advancements in Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) this research attempts to improve user understanding of key topics discussed in meetings by combining ASR models with NLP tools to create a visual summary that would improve user understanding of key topics covered during meetings. To achieve this the research utilizes the speech-to-text transcription and speaker identification capabilities of the WhisperX model with noun phrase extraction features provided by Spacy and key topic recognition functionality of Microsoft's DeBERTa model. Finally, the data is presented as a node-based graph utilizing the D3.js library. The results show that the system is able to identify between 33% - 58% of meeting key topics. This shows the potential of combining ASR models with NLP tools for creating concise meeting summaries but also raises new questions such as why some topics were missed, how the system performance can be improved, and how to design an optimal user interface for such a task.