Evaluating Interactive Summarization: an Expansion-Based Framework
Ori Shapira, Ramakanth Pasunuru, Hadar Ronen, Mohit Bansal, Yael, Amsterdamer, Ido Dagan

TL;DR
This paper introduces an end-to-end evaluation framework for expansion-based interactive multi-document summarization, incorporating real user sessions and adapted metrics to improve and compare interactive summarization methods.
Contribution
The paper develops a comprehensive evaluation framework for interactive summarization, including real user data collection and standardized, interaction-aware metrics, and provides baseline implementations for benchmarking.
Findings
Framework effectively captures user interaction dynamics.
Baseline systems demonstrate the framework's applicability.
Evaluation results support the framework's validity and usefulness.
Abstract
Allowing users to interact with multi-document summarizers is a promising direction towards improving and customizing summary results. Different ideas for interactive summarization have been proposed in previous work but these solutions are highly divergent and incomparable. In this paper, we develop an end-to-end evaluation framework for expansion-based interactive summarization, which considers the accumulating information along an interactive session. Our framework includes a procedure of collecting real user sessions and evaluation measures relying on standards, but adapted to reflect interaction. All of our solutions are intended to be released publicly as a benchmark, allowing comparison of future developments in interactive summarization. We demonstrate the use of our framework by evaluating and comparing baseline implementations that we developed for this purpose, which will…
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Taxonomy
TopicsTopic Modeling · Advanced Text Analysis Techniques · Natural Language Processing Techniques
