Cross-Document Non-Fiction Narrative Alignment

Miller, Ben; Olive, Jennifer; Gopavaram, Shakthidhar; Shrestha, Ayush

doi:10.18653/v1/w15-4509

Cited by 5 publications

(12 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Both processes begin with a set of n articles to be segmented by event. This segmentation is done using EVITA as documented in (Miller et al, 2015). The result is a document segmented into a highly granular event sequence.…”

Section: Methodsmentioning

confidence: 99%

“…The adjacency matrix similarity measurement method used is as per (Miller et al, 2015), which was inspired by Blondel et al's HITS (Hyperlink-Induced Topic Search) algorithm (Blondel et al, 2004). Hypernym sequence similarity of narrative units proceeded by pairwise comparison of all sequences across all articles.…”

Section: Creation Of Similarity Matricesmentioning

confidence: 99%

“…Entity similarity measurement proceeded according to the methodology detailed in (Miller et al, 2015). That methodology builds a 3D matrix of the adjacency matrices where the axes from these individual matrices compose the first two dimensions and the event number composes the third dimension.…”

Section: Creation Of Similarity Matricesmentioning

confidence: 99%

“…A coherent narrative textual unit describes a section of text that can be segmented from its surroundings while still describing a possibility, an act, and a result, a definition consistent with (Bal, 1997). Research on aligning these sections, or narrative frames, has been pursued in various domains (Prud'hommeaux and Roark, 2012) (Miller et al, 2015) (Reiter, 2014); this paper describes preliminary work extending that work to identify moments of narratological similarity but in the domain of non-fiction news articles.…”

Section: Introductionmentioning

confidence: 99%

“…To that end, we propose an expansion to a method for cross-document coreference of narrative units described in (Miller et al, 2015) that focused on the cross-document coreference of character and location entities. That method identified events in free text using EVITA (Saurí et al, 2005) then built adjacency matrices capturing entity-entity co-occurrence for each event.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Proceedings of the First Workshop on Computing News Storylines

Caselli

Erp

Minard

et al. 2015

View full text Add to dashboard Cite

at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study of storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters' perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)). The majority of the previous work on narratives and narrative structures have mainly focused on the analysis of fictitious texts. However, modern day news reports still reflect this narrative structure, but they have proven difficult for automatic tools to summarise, structure, or connect to other reports. This difficulty is partly rooted in the fact that most text processing tools focus on extracting relatively simple structures from the local lexical environment, and concentrate on the document as a unit or on even smaller units such as sentences or phrases, rather than cross-document connections. However, current information needs demand a move towards multidimensional and distributed representations which take into account the connections between all relevant elements involved in a "story". Additionally, most work on cross-document temporal processing focuses on linear timelines, i.e. representations of chronologically ordered events in time (for instance, the Event Narrative Event Chains by Chambers (2011), or the SemEval 2015 Task 4: Cross Document TimeLines by ). Storylines, though, are more complex, and must take into account temporal, causal and subjective dimensions. How storylines should be represented and annotated, how they can be extracted automatically, and how they can be evaluated are open research questions in the NLP and AI communities. The workshop aimed to bring together researchers from different communities working on representing and extracting narrative structures in news, a text genre which is highly used in NLP but which has received little attention with respect to narrative structure, representation and analysis. Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams. Policy makers, NGOs, information specialists (such as journalists and librarians) and others are increasingl...

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Creation Of Similarity Matricesmentioning

confidence: 99%

Section: Creation Of Similarity Matricesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Proceedings of the First Workshop on Computing News Storylines

Caselli

Erp

Minard

et al. 2015

View full text Add to dashboard Cite

show abstract

A method for cross-document narrative alignment of a two-hundred-sixty-million word corpus

Miller

Olive

Gopavaram

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

Self Cite

View full text Add to dashboard Cite

Identifying similar narrative sections across longer documents would help identify key events within a corpus, enrich understanding of those events, provide a mechanism for organizing corpora according to their event content, and allow for bottom-up testing of theories of narrative. This paper proposes an automated method for narrative alignment across large textual corpora using techniques from natural language processing and similarity-based image segmentation. This method proceeds by segmenting each document into a series of events, constructs sequences of abstracted representations of those events, compares pairs of sequences to generate image matrices, segments the images, identifies similar segments to discover commonly occurring narrative units, and, finally, returns the source sentences to make the clusters of narrative similarity readable. Preliminary tests of elements of this method were conducted on a small heterogeneous corpus (< 100 documents) and a moderate heterogeneous corpus ( 10k documents). Further implementation as described in this position paper is necessary to scale to the full 251k document corpus from which the moderate corpus was drawn.

show abstract

SemEval-2022 Task 8: Multilingual news article similarity

Chen¹,

Zeynali²,

Camargo³

et al. 2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

View full text Add to dashboard Cite

Thousands of new news articles appear daily in outlets in different languages. Understanding which articles refer to the same story can not only improve applications like news aggregation but enable cross-linguistic analysis of media consumption and attention. However, assessing the similarity of stories in news articles is challenging due to the different dimensions in which a story might vary, e.g., two articles may have substantial textual overlap but describe similar events that happened years apart. To address this challenge, we introduce a new dataset of nearly 10,000 news article pairs spanning 18 language combinations annotated for seven dimensions of similarity as SemEval 2022 Task 8. Here, we present an overview of the task, the best performing submissions, and the frontiers and challenges for measuring multilingual news article similarity. While the participants of this SemEval task contributed very strong models, achieving up to 0.818 correlation with gold standard labels across languages, human annotators are capable of reaching higher correlations, suggesting space for further progress.

show abstract

Cross-Document Non-Fiction Narrative Alignment

Cited by 5 publications

References 9 publications

Proceedings of the First Workshop on Computing News Storylines

Proceedings of the First Workshop on Computing News Storylines

A method for cross-document narrative alignment of a two-hundred-sixty-million word corpus

SemEval-2022 Task 8: Multilingual news article similarity

Contact Info

Product

Resources

About