Archive T ime L ine Summarization ( ATLS ): Conceptual Framework for Timeline Generation over Historical Document Collections (2022)

Abstract: Archive collections are nowadays mostly available through search engines interfaces, which allow a user to retrieve documents by issuing queries. The study of these collections may be, however, impaired by some aspects of search engines, such as the overwhelming number of documents returned or the lack of contextual knowledge provided. New methods that could work independently or in combination with search engines are then required to access these collections. In this position paper, we propose to extend TimeLine Summarization (TLS) methods on archive collections to assist in their studies. We provide an overview of existing TLS methods and we describe a conceptual framework for an Archive TimeLine Summarization (ATLS) system, which aims to generate informative, readable and interpretable timelines.

## introduction 1.exploring archives in the recent years, archives and libraries across the world have frequently conducted digitization campaigns of their collections. this first opened access to thousands of historical documents to a wider public, but also propelled the emergence of new research fields such as digital humanities and digital history. these collections are usually accessible through search engines, which return documents relevant to a query specified by the user. unfortunately, standard search engines are not fully suited to assist users in exploring historical collections such as news archives where temporal aspects of documents play a key role. firstly, search engines return documents by their relevance to the query, typically without considering the chronological or causal relations between them, which may prevent the user from understanding the interrelations between events. furthermore when exploring such documents, the user might lack the contextual knowledge to understand the events that are mentioned in them. this is especially true when exploring news archives coming from distant pasts or exploring longitudinal collections, i.e. which span over a long time frame such as decades or centuries. search engines do not seem to consider the importance of an event mention for a given query, thus less important events might be returned by the system, especially for broad queries. improved search engines are then required to study such collections. ## https://contamehistorias.pt/arquivopt to our knowledge, there is no study on the impact of ocr on tls, but we can assume it will be similar. furthermore, archive collections may also differ from contemporary data because of their temporal context: orthographic rules may differ, places might have changed names @xcite or concepts may have acquired another meaning. most existing annotated resources necessary for nlp components such as ner or ed are created on contemporary data. historical documents archives are thus harder to process because of this lack of suitable annotated resources. most tls methods generate timelines through statistical analysis of the input dataset. they also often require that the input corpus contains documents of a similar type and similar content. however, an archive collection may be heterogeneous and contain documents of different authors, genres, topics and periods. it may also be fragmentary and not as complete as a contemporary dataset. finally, although the timelines generated by tls systems are often easy to read, the process that created them is often not made explicit. if timelines must assist the study of historical datasets by highlighting important events, they must be interpretable and explain why these events are deemed important. in this position paper, we propose to extend time-line summarization (tls) methods to assist in the studies of archive collections. we first present an overview of existing tls methods. we then describe a conceptual framework for an archive time-line summarization (atls) system, which aims to generate informative, readable and interpretable timelines, before suggesting several methods to implement it. this paper is organized as follows: in section 2 we present an overview of existing timeline summarization methods. in section 3 and 4, we respectively describe our conceptual framework and discuss some of its potential applications. finally, we present our conclusion in section 5, alongside possibilities for future works. tls methods in general tend to be applied to summarize datasets describing large events, such as the egyptian protests or the syrian war @xcite . these methods require that the dataset covers a constrained period of time and is homogeneous, i.e. that the documents cover the same topic. standard tls methods are thus not suited to summarize heterogeneous or longitudinal datasets. some works such as @xcite al. (2014 qtls generally consists in the two following steps: event detection and event ranking. to detect events, chieu and lee (2004) select any sentence where the terms of the query appear, @xcite cluster by a common date every sentence returned by the query and @xcite detect peaks of date occurrences in the time span covered by the documents. other works train a classifier to detect important events @xcite or rank events by their importance with a learningto-rank model @xcite . however, these classifiers need training data, which are difficult to create since defining what is important is a subjective matter. this can lead to disappointing results as shown in @xcite . to determine the importance of events, @xcite first score them according to their relevancy and saliency to the query, then rerank them to ensure a diverse timeline. @xcite rank the importance of a sentence according to their "interest" and "burstiness", then remove duplicate sentences to ensure diversity. @xcite use the keyword extractor yake! @xcite to weight the terms in the event description. duplicate event descriptions are detected with the levenshtein similarity measure and removed. those methods finally select the top most important events to generate the timeline. in order to generalize the application of tls, @xcite propose a multiple timeline summarization (mtls) system, which generates a timeline for each story found in the dataset. to do so, it first detects events mentioned in the dataset and measures their saliency and consistency. an event linking step determines the link between these events in order to generate each timeline. similarly, @xcite propose the comparative time-line summarization (ctls) task, which generates a comparative timeline highlighting the contrast between two timestamped timeline documents (e.g. biographies, historical sections, ...) by computing local and global importance of events. there are few datasets for the tls task such as 17 timelines (t17) @xcite , crisis @xcite , entities (gholipour ghalandari and ifrim, 2020), covidtls (la quatra et al., 2021) or tls-covid19 @xcite which are constructed from contemporary news articles. however, datasets are often lacking in most projects. it is then necessary to create a dataset from scratch as in @xcite al. (2015 due to this lack of datasets, evaluating tls systems is a difficult task. the date selection step can be evaluated with the f1-measure @xcite or with the mean average precision (map) metric @xcite . the date summary is often evaluated with one of the rouge metrics @xcite to compare a ground-truth timeline and a generated one @xcite @xcite . methods relying on event detection such as ge et al. (2015 ## discussion in this section, we describe two hypothetical use cases comparing the application of tls and atls systems, and compare in ## conclusion timeline summarization can be a useful tool for getting an overview of historical collections as well as it can serve as a novel information access means to news article archives. in this position paper, we have presented an overview of existing tls methods and described a conceptual framework for archive timeline summarization systems. the implementation of the framework outlined in this paper will be the subject of our future work. we also intend to ask humanities scholars (historians, archivists, ...) to evaluate the quality of generated timelines and the effectiveness of our framework for the study of archive collections.