The objective of this research is to design and develop a novel human-AI collaborative framework that can braid the Overlapping, Unique, and Conflicting information from a pair of alternative narratives into a single coherent summary.

Overview of Project CAMPeN

Multi-Perspective Narratives (MPNs) are ubiquitous and very useful for verifying information from different alternative narratives, and thus, MPNs facilitate more informed decisions by providing a concise overall picture of the current situation. Despite great progress in the area of natural language processing (NLP), computers are still far from being able to analyze multi-perspective narratives accurately; addressing this limitation is the focus of this project.

In this ongoing project, we are developing a novel human-AI collaborative framework called CAMPeN (``Collaborative Analytics of Multi-Perspective Narratives’’), where the AI, given multiple alternative narratives as input, first extracts a set of candidate clauses w.r.t. the Overlap-Unique-Conflict criteria, separately, in a zero-shot fashion. Next, the human actively verifies clauses that were labeled with low confidence by the AI. Finally, the machine braids the high-confidence/verified clauses to construct the ultimate Overlap-Unique-Conflict style summary, which will be presented to the user. The major benefits of the proposed framework are two-fold: 1) it enables domain experts in fields other than machine learning/NLP (e.g., a military general) to quickly dig out/verify interesting hypotheses from multiple alternative narratives/descriptions without worrying about the underlying computational techniques and thus, democratizes AI, and 2) it can quickly verify facts and claims about real-world events by analyzing alternative narratives and braid them into a single narrative with a higher degree of Information Assurance.

This project adopts both zero-shot and reinforcement learning approaches for extracting overlapping, unique, and conflicting information from alternative narratives that can be trained in a self-supervised fashion without requiring a large collection of training data; therefore, the proposed framework needs minimal human supervision in comparison to the existing Multi-Document Summarization techniques. Additionally, the project borrows intuitions and insights from the classical set theory and applies the properties of set operators to develop novel reward/loss functions to enable effective training of reinforcement learning-based extraction networks.

Objectives of Project CAMPeN

The proposed work includes the following objectives.

1. Design and develop a novel human-AI collaborative framework that can braid the Overlapping, Unique, and Conflicting information from a pair of alternative narratives into a single coherent summary.
2. Conduct research on how to extract overlapping information from a pair of alternative narratives and paraphrase the overlap.
3. Given a pair of alternative narratives as inputs, conduct research on how to extract unique information present exclusively in each input narrative and identify interesting, unique information.
4. Conduct research on how to extract conflicting information from a pair of alternative narratives and how to resolve the conflict via effective human-AI collaboration.
5. Design and Develop novel metrics for quantifying Author Influence by applying the proposed Human-AI collaborative framework.
6. Conduct a thorough quantitative and qualitative evaluation of the proposed human-AI collaborative framework.
7. Determine whether the proposed Human-AI collaborative framework performs similarly or differently in a language other than English.