KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces
- URL: http://arxiv.org/abs/2404.17597v1
- Date: Mon, 22 Apr 2024 15:01:39 GMT
- Title: KamerRaad: Enhancing Information Retrieval in Belgian National Politics through Hierarchical Summarization and Conversational Interfaces
- Authors: Alexander Rogiers, Maarten Buyl, Bo Kang, Tijl De Bie,
- Abstract summary: KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information.
The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI.
- Score: 55.00702535694059
- License:
- Abstract: KamerRaad is an AI tool that leverages large language models to help citizens interactively engage with Belgian political information. The tool extracts and concisely summarizes key excerpts from parliamentary proceedings, followed by the potential for interaction based on generative AI that allows users to steadily build up their understanding. KamerRaad's front-end, built with Streamlit, facilitates easy interaction, while the back-end employs open-source models for text embedding and generation to ensure accurate and relevant responses. By collecting feedback, we intend to enhance the relevancy of our source retrieval and the quality of our summarization, thereby enriching the user experience with a focus on source-driven dialogue.
Related papers
- Increasing faithfulness in human-human dialog summarization with Spoken Language Understanding tasks [0.0]
We propose an exploration of how incorporating task-related information can enhance the summarization process.
Results show that integrating models with task-related information improves summary accuracy, even with varying word error rates.
arXiv Detail & Related papers (2024-09-16T08:15:35Z) - Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems.
KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities.
KERL achieves state-of-the-art results in both recommendation and response generation tasks.
arXiv Detail & Related papers (2023-12-18T06:41:23Z) - FREDSum: A Dialogue Summarization Corpus for French Political Debates [26.76383031532945]
We present a dataset of French political debates for the purpose of enhancing resources for multi-lingual dialogue summarization.
Our dataset consists of manually transcribed and annotated political debates, covering a range of topics and perspectives.
arXiv Detail & Related papers (2023-12-08T05:42:04Z) - Does Collaborative Human-LM Dialogue Generation Help Information
Extraction from Human Dialogues? [55.28340832822234]
Problem-solving human dialogues in real applications can be much more complex than existing Wizard-of-Oz collections.
We introduce a human-in-the-loop dialogue generation framework capable of synthesizing realistic dialogues.
arXiv Detail & Related papers (2023-07-13T20:02:50Z) - Multi-grained Hypergraph Interest Modeling for Conversational
Recommendation [75.65483522949857]
We propose a novel multi-grained hypergraph interest modeling approach to capture user interest beneath intricate historical data.
In our approach, we first employ the hypergraph structure to model users' historical dialogue sessions and form a session-based hypergraph, which captures coarse-grained, session-level relations.
We further conduct multi-grained hypergraph convolution on the two kinds of hypergraphs, and utilize the enhanced representations to develop interest-aware CRS.
arXiv Detail & Related papers (2023-05-04T13:13:44Z) - FCC: Fusing Conversation History and Candidate Provenance for Contextual
Response Ranking in Dialogue Systems [53.89014188309486]
We present a flexible neural framework that can integrate contextual information from multiple channels.
We evaluate our model on the MSDialog dataset widely used for evaluating conversational response ranking tasks.
arXiv Detail & Related papers (2023-03-31T23:58:28Z) - Enhancing Semantic Understanding with Self-supervised Methods for
Abstractive Dialogue Summarization [4.226093500082746]
We introduce self-supervised methods to compensate shortcomings to train a dialogue summarization model.
Our principle is to detect incoherent information flows using pretext dialogue text to enhance BERT's ability to contextualize the dialogue text representations.
arXiv Detail & Related papers (2022-09-01T07:51:46Z) - Grounding in social media: An approach to building a chit-chat dialogue
model [9.247397520986999]
Building open-domain dialogue systems capable of rich human-like conversational ability is one of the fundamental challenges in language generation.
Current work on knowledge-grounded dialogue generation primarily focuses on persona incorporation or searching a fact-based structured knowledge source such as Wikipedia.
Our method takes a broader and simpler approach, which aims to improve the raw conversation ability of the system by mimicking the human response behavior on social media.
arXiv Detail & Related papers (2022-06-12T09:01:57Z) - Retrieval-Free Knowledge-Grounded Dialogue Response Generation with
Adapters [52.725200145600624]
We propose KnowExpert to bypass the retrieval process by injecting prior knowledge into the pre-trained language models with lightweight adapters.
Experimental results show that KnowExpert performs comparably with the retrieval-based baselines.
arXiv Detail & Related papers (2021-05-13T12:33:23Z) - Exploring Recurrent, Memory and Attention Based Architectures for
Scoring Interactional Aspects of Human-Machine Text Dialog [9.209192502526285]
This paper builds on previous work in this direction to investigate multiple neural architectures.
We conduct experiments on a conversational database of text dialogs from human learners interacting with a cloud-based dialog system.
We find that fusion of multiple architectures performs competently on our automated scoring task relative to expert inter-rater agreements.
arXiv Detail & Related papers (2020-05-20T03:23:00Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.