PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets Stream

TL;DR

PDSum introduces a prototype-driven, unsupervised approach for continuously summarizing evolving multi-document streams, effectively capturing relevant and novel information over time.

Contribution

This work defines the EMDS problem and proposes PDSum, a novel algorithm that maintains lightweight prototypes to adapt to document changes while preserving previous knowledge.

Findings

PDSum outperforms existing algorithms in relevance, novelty, and distinctiveness.

It is robust across various evaluation settings.

PDSum efficiently updates summaries in real-time streams.

Abstract

Summarizing text-rich documents has been long studied in the literature, but most of the existing efforts have been made to summarize a static and predefined multi-document set. With the rapid development of online platforms for generating and distributing text-rich documents, there arises an urgent need for continuously summarizing dynamically evolving multi-document sets where the composition of documents and sets is changing over time. This is especially challenging as the summarization should be not only effective in incorporating relevant, novel, and distinctive information from each concurrent multi-document set, but also efficient in serving online applications. In this work, we propose a new summarization problem, Evolving Multi-Document sets stream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSum with the idea of prototype-driven continuous summarization. PDSum builds a lightweight prototype of each multi-document set and exploits it to adapt to new documents while preserving accumulated knowledge from previous documents. To update new summaries, the most representative sentences for each multi-document set are extracted by measuring their similarities to the prototypes. A thorough evaluation with real multi-document sets streams demonstrates that PDSum outperforms state-of-the-art unsupervised multi-document summarization algorithms in EMDS in terms of relevance, novelty, and distinctiveness and is also robust to various evaluation settings.