MegraTLS: Pretraining a Prefabricated PlugIN via Meta-Graph Autoencoder for Timeline Summarization System

Timeline summarization (TLS) system involves creating a concise overview of extended daily summaries through sentence and date selection, employing predefined unlearnable representations for these elements. This poses a unique challenge, as it involves normal representations that limit the performance of the downstream daily summarization tasks. Previous research in this field separately encoded dates and sentences in independent learning representation spaces, hindering the integration of their relationships and the attainment of a globally optimal summary by the TLS system. In this study, our emphasis is on pretraining a document encoder for the TLS system, where we introduce the concept of representing each document as a plug-and-play document plugin (PlugIN). Specifically, the proposed MegraTLS (Meta-graph autoencoder based TimeLine Summarization System) is based on a modified meta-graph autoencoder network. By incorporating the prefabricated PlugIN into the backbone of theDateWiseTLS framework for downstream timeline summarization, we streamline the encoding of a document, enabling efficient handling of date and sentence selection within a compact representation space in date and sentence embeddings. The experimental results demonstrate that our MegraTLS prominently promotes existing TLS frameworks in timeline summarization performance. Besides, the MegraTLS exhibits robustness and insensitivity to parameter changes. Case studies of financial events illustrate that our model accurately captures key timeline events when applied to specific fields, providing more industry-specific valuable information than ground truth while maintaining simplicity. Although our experiments are conducted exclusively on Chinese datasets, the methodological insights and empirical findings of this work offer transferable, actionable, and robust guidance for deploying timeline summarization technology in domain-specific industrial applications.