Timeline Summarization based on Event Graph Compression via Time-Aware Optimal Transport (2021)

Abstract: Timeline Summarization identifies major events from a news collection and describes them following temporal order, with key dates tagged. Previous methods generally generate summaries separately for each date after they determine the key dates of events. These methods overlook the events’ intra-structures (arguments) and inter-structures (event-event connections). Following a different route, we propose to represent the news articles as an event-graph, thus the summarization becomes compressing the whole graph to its salient sub-graph. The key hypothesis is that the events connected through shared arguments and temporal order depict the skeleton of a timeline, containing events that are semantically related, temporally coherent and structurally salient in the global event graph. A time-aware optimal transport distance is then introduced for learning the compression model in an unsupervised manner. We show that our approach significantly improves on the state of the art on three real-world datasets, including two public standard benchmarks and our newly collected Timeline100 dataset.

## introduction timeline summarization @xcite @xcite @xcite @xcite @xcite aims at generating a sequence of major news events with their key dates from a large collection of related news from multiple perspectives (see figure 1 for an example). the timeline summarization task poses several challenges to existing natural language processing (nlp) techniques: (1) in contrast to multi-document summarization (mds) dealing with tens of documents @xcite , it summarizes hundreds of long documents, which requires the model to efficiently maintain a joint representation of the entire news collection, so that the summary has its coverage and coherence optimized globally. (2) the summary is expected to select key dates and capture the temporal interdependency across key stories, which, compared to standard mds, poses additional challenges in reconstructing temporal order. (3) manual labeling of timeline summaries is costly; thus the labeled data for model training is very limited. as a result, previous studies @xcite usually take an unsupervised approach. specifically, these methods first identify the key dates from the publication time distribution. then for each key date and its associated news articles, a summary is generated based on the salient sentences measured by the inter-similarity of these articles. in these methods, the document representations are limited to local text features, ignoring the global context of the news collection. the applications of neural models, especially advanced pre-trained language models, such as bert @xcite and @xcite , are restricted in terms of both representation capacity and memory efficiency when handling the global context within such input document size. we propose an event graph representation along with compression to deal with the representation difficulties in global graph contextualization, scalability, and time-awareness. our solution consists of the following key ideas. injure unconscious transport called to paramedics house / mansion michael jackson transport travel die die/collapse dr conrad murray meet speak meet interview police correspondence call transport release family agent los angeles ambulance ucla medical center / hospital born born 2009-06-27 police also want to speak to jackson's doctor who witnessed his collapse .... on friday night thousands of fans came together in london's trafalgar square to light candles and sing some of his biggest hits .... 1958-09-29 june25 last thursday friday night friday night 2009-06-26 2009-06-26 2009-06-25 2009-06-25 attack raid 2009-07-22 attack raid 2009-07-28 home clinic 2009-06-28 2009-06-25 event graph encoder differentiable graph compression to select top m nodes event graph encoder extractive summarization input documents