Multi- T ime L ine Summarization ( MTLS ): Improving Timeline Summarization by Generating Multiple Summaries (2021)

Abstract: In this paper, we address a novel task, Multiple TimeLine Summarization (MTLS), which extends the flexibility and versatility of Time-Line Summarization (TLS). Given any collection of time-stamped news articles, MTLS automatically discovers important yet different stories and generates a corresponding time-line for each story. To achieve this, we propose a novel unsupervised summarization framework based on two-stage affinity propagation. We also introduce a quantitative evaluation measure for MTLS based on previousTLS evaluation methods. Experimental results show that our MTLS framework demonstrates high effectiveness and MTLS task can give bet-ter results than TLS.

## introduction nowadays, online news articles are one of the most popular web documents. however, due to a huge amount of news articles available online, it is getting difficult for users to effectively search, understand, and track the entire news stories. to solve this problem, a research area of timeline summarization (tls) has been established, which can alleviate the redundancy and complexity inherent in news article collections thereby helping users better understand the news landscape. after the influential work on temporal summaries by @xcite , tls has attracted researchers' attention. most of works on tls @xcite @xcite have focused on improving the performance of summarization. however, their drawbacks are as follows: (a) the methods work essentially on a homogeneous type of datasets such as ones compiled from the search results of an unambiguous query (e.g., "bp oil spill"). the requirements imposed on the input dataset make it hard for tls systems to generalize; (b) the output is usually a single timeline regardless of the size and the complexity of the input dataset. we propose here the multiple timeline summarization (mtls) task that enhances and further generalizes tls. mtls automatically generates a set of timelines that summarize disparate yet important stories, rather than always generating a single timeline as is in the case of tls. an effective mtls framework should: (a) detect key events including both short-and long-term events, (b) link events related to the same story and separate events belonging to other stories, and (c) provide informative summaries of constituent events to be incorporated into the generated timelines. mtls can also help to deal with the ambiguity, which is common in information retrieval. for example, suppose that a user wants to get an overview of news about a basketball player, michael jordan, from a large collection of news articles. however, when a search engine over such a collection takes "michael jordan" as a query, it would likely return documents constituting a mixture of news about different persons having the same name. then, how can a typical tls system return meaningful results if only a single timeline can be generated? similarly, ambiguous queries such as "apple", "amazon", "java" require mtls solutions to produce high quality results. to address this task, we further propose a two-stage affinity propagation summarization framework (2saps). it uses temporal information embedded in sentences to discover important events, and their linking information latent in news articles to construct timelines. 2saps has several advantages: firstly, it is entirely unsupervised which is especially suited to tls-related tasks as there are very few gold summaries available for training supervised systems; secondly, both the number of events and the number of generated timelines are self-determined. this allows our framework to be dependent only on the input document collection, instead of on human efforts. furthermore, the current tls evaluation measures allow only 1-to-1 comparison (system-to human-generated timeline), which is not suitable for mtls task where multiple timelines must be compared to (typically) multiple ground-truth timelines. therefore, we also propose a quantitative evaluation measure for mtls based on the adaptation of the previous tls evaluation framework. given these points, our contributions in this work are summarized as follows: ## problem definition we formulate mtls task as follows: input: a time-stamped news article collection @xmath0, d 2 , ..., d |d| }. the collection can be standalone or compiled from search results returned by a news search engine. output: a set of timelines, @xmath1, t 2 , . . . , t k } is generated based on d, so that each timeline t i includes a sequence of time/date foot_0 and summary pairs (t tasks output 1 timeline @xmath2.1) we note that while the traditional tls task is limited as a document collection for it is typically coherent and homogeneous, mtls is more flexible as the input news collection can be diverse. for example, the input collection can be generated using a search query q composed of multiple entities or concepts like @xmath3, "h1n1", "iraq"} or by using an ambiguous query like @xmath4, "jordan"}, or it can also consist of news articles crawled over a certain time span from multiple news sources. generally, the more heterogeneous d is, the more timelines could be produced. the intuition behind this idea is that users will need more structured information to help them understand a relatively complex document collection. ## framework next, we present two key components of our framework: event generation module (sec. 4.1) and timeline generation module (sec. 4.2). we first make the following two assumptions: assumption 1: news articles sometimes retrospectively mention past events for providing necessary context to the target event, for underlying continuation, causality, etc. assumption 2: sentences mentioning similar dates have higher probability to refer to the same event than sentences with different dates. ## experimental setup the goal of our experiments is to answer the following research questions (rqs): rq1: do mtls models produce more meaningful output than tls models? rq2: how does 2saps framework perform on mtls task compared with other mtls baselines? rq3: how effective are the components of the modules in 2saps? how do parameter changes in the model affect the results? ## conclusions we introduced mtls task to generalize the timeline summarization problem. mtls improves the performance of timeline summarization by generating multiple summaries. we conducted experiments to first show that given a heterogeneous time-stamped news article collection, tls usually does not produce satisfactory result. we further proposed 2saps, a two-stage clustering-based framework, to effectively solve mtls task. furthermore, we extended tls datasets to mtls datasets, as well as introduced a novel evaluation measure for mtls. experimental results show that 2saps outperforms mtls baselines which follow the "divide-and-summarize" strategy. our work significantly improves the generalization ability of timeline summarization and can provide users with easier access to news collections. as an unsupervised approach that does not require costly training data, it can be applied to any potential datasets and languages. in future work, we plan to test our approach on additional mtls datasets. we will also investigate scenarios in which mtls can enhance information retrieval systems operating over news article collections. for users searching over large temporal collections, structuring the returned results into a series of timelines could prove beneficial, instead of returning a usual list of interwoven documents that relate to different stories or periods.