Two-phase Multi-document Event Summarization on Core Event Graphs

Abstract

Succinct event description based on multiple documents is critical to news systems as well as search engines. Different from existing summarization or event tasks, Multi-document Event Summarization (MES) aims at the query-level event sequence generation, which has extra constraints on event expression and conciseness. Identifying and summarizing the key event from a set of related articles is a challenging task that has not been sufficiently studied, mainly because online articles exhibit characteristics of redundancy and sparsity, and a perfect event summarization needs high level information fusion among diverse sentences and articles. To address these challenges, we propose a two-phase framework for the MES task, that first performs event semantic graph construction and dominant event detection via graph-sequence matching, then summarizes the extracted key event by an event-aware pointer generator. For experiments in the new task, we construct two large-scale real-world datasets for training and assessment. Extensive evaluations show that the proposed framework significantly outperforms the related baseline methods, with the most dominant event of the articles effectively identified and correctly summarized.

Graphmax for Text Generation

“…Traditional natural language generation methods rely on a large corpus, which is typically expensive to train. To address this issue, researchers have explored a new paradigm of pre-trained LMs, such as BERT (Devlin et al, 2019), GPT-2 (Radford et al, 2019), and BART (Lewis et al, 2020;Koto et al, 2020;Chen et al, 2022;Vougiouklis et al, 2020). By incorporating additional domain-specific codes (Keskar et al, 2019), such as sentiment labels (Dathathri et al, 2020) or attribute vectors (Yu, Yu, & Sagae, 2021), the goal is to modify the pre-trained LM with little fine-tuning cost.…”