Discourse as a Function of Event: Profiling Discourse Structure in News Articles around the Main Event (2020)

Abstract: Understanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event. To enable computational modeling of news structures, we apply an existing theory of functional discourse structure for news articles that revolves around the main event and create a human-annotated corpus of 802 documents spanning over four domains and three media sources. Next, we propose several document-level neural-network models to automatically construct news content structures. Finally, we demonstrate that incorporating system predicted news structures yields new state-of-the-art performance for event coreference resolution. The news documents we annotated are openly available and the annotations are publicly released for future research.

## introduction detecting and incorporating discourse structures is important for achieving text-level language understanding. several well-studied discourse analysis tasks, such as rst @xcite and pdtb style @xcite discourse parsing and text segmentation @xcite , generate rhetorical and content structures that have been shown useful for many nlp applications. but these widely applicable discourse structures overlook genre specialties. in this paper, we focus on studying content structures specific to news articles, a broadly studied text genre for many nlp tasks and applications. we believe that genre-specific discourse structures can effectively complement genre independent discourse structures and are essential for achieving deep story-level text understanding. what is in a news article? normally, we expect a news article to describe well verified facts of newly 1 dataset can be found at https://github.com/ prafulla77/discourse_profiling happened events, aka the main events. however, almost no news article limits itself to reporting only the main events. most news articles also report context-informing contents, including recent precursor events and current general circumstances, that are meant to directly explain the cause or the context of main events. in addition, they often contain sentences providing further supportive information that is arguably less relevant to main events, comprising of unverifiable or hypothetical anecdotal facts, opinionated statements, future projections and historical backgrounds. apparently, the relevance order of sentences is not always aligned with their textual order, considering that sentences in a news article are ordered based on their vague importance that is generally determined by multiple factors, including content relevance as well as other factors such as the focus of an article, the author's preferences and writing strategies. while a number of theoretical studies for news discourse exist, little prior effort has been put on computational modeling and automatic construction of news content structures. we introduce a new task and a new annotated text corpus for profiling news discourse structure that categorizes contents of news articles around the main event. the newsdiscourse corpus consists of 802 news articles (containing 18,155 sentences), sampled from three news sources (nyt, xinhua and reuters), and covering four domains (business, crime, disaster and politics). in this corpus, we label each sentence with one of eight content types reflecting common discourse roles of a sentence in telling a news story, following the news content schemata proposed by van dijk @xcite with several minor modifications. next, we present several baselines for automatically identifying the content type of sentences. the experimental results show that a decent performance can be obtained using a basic neural network-based multi-way classification approach. the sentence classification performance can be further improved by modeling interactions between sentences in a document and identifying sentence types in reference to the main event of a document. we envision that the news discourse profiling dataset as well as the learnt computational systems are useful to many discourse level nlp tasks and applications. as an example, we analyze correlations between content structures and event coreference structures in news articles, and conduct experiments to incorporate system predicted sentence content types into an event coreference resolution system. specifically, we analyze the lifespan and spread of event coreference chains over different content types, and design constraints to capture several prominent observations for event coreference resolution. experimental results show that news discourse profiling enables consistent performance gains across all the evaluation metrics on two benchmark datasets, improving the previous best performance for the challenging task of event coreference resolution. ## related work several well-studied discourse analysis tasks have been shown useful for many nlp applications. the rst @xcite @xcite @xcite and pdtb style @xcite @xcite @xcite discourse parsing tasks identify discourse units that are logically connected with a predefined set of rhetorical relations, and have been shown useful for a range of nlp applications such as text quality assessment @xcite , sentiment analysis @xcite ), text summarization @xcite , machine translation @xcite ) and text categorization @xcite . text segmentation @xcite @xcite is another well studied discourse analysis task that aims to divide a text into a sequence of topically coherent segments and has been shown useful for text summarization @xcite , sentiment analysis @xcite and dialogue systems @xcite . the news discourse profiling task is complementary to the well-established discourse analysis tasks and is likely to further benefit many nlp applications. first, it studies genre-specific discourse structures, while the aforementioned discourse analysis tasks study genre independent general discourse structures and thus fail to incorporate domain knowledge. second, it focuses on understanding global content organization structures with the main event at the center, while the existing tasks focus on either understanding rhetorical aspects of discourse structures (rst and pdtb discourse parsing) or detecting shallow topic transition structures (text segmentation). genre-specific functional structures have been studied based on different attributes, but mostly for genres other than news articles. @xcite , @xcite and @xcite used rhetorical status and argumentation type to both define functional theories and create corpora for scientific articles. @xcite , @xcite , @xcite and @xcite extensively studied functional structures in biological domain with multiple new annotation schemata. past studies on functional structures of news articles have been mainly theoretical. apart from van dijk's theory of news discourse @xcite , @xcite proposed framing-based approach along four structural dimensions: syntactic, script, thematic and rhetorical, of which syntactic structure is similar to the dijk's theory. owing to the high specificity of the dijk's theory, @xcite performed a pilot study for its computational feasibility and annotated a small dataset of 50 documents taken from the ace phase 2 corpus @xcite . however, as mentioned in the paper, their annotators were given minimal training prior to annotations, consequently, the kappa inter-agreement (55%) between two annotators was not satisfactory. in addition, coverage of their annotated dataset on broad event domains and media sources was unclear. the only studies on functional structure of news article with sizable dataset include @xcite that coarsely separates narration from descriptive contents and @xcite that classify clauses based on their aspectual property. ## elements of discourse profiling we consider sentences to be units of discourseand define eight schematic categories to study their roles within the context of the underlying topic. the original van dijk's theory was designed for ## dataset creation and statistics the newsdiscourse corpus consists of 802 openly accessible news articles containing 18,155 sentences foot_0 annotated with one of the eight content types or n/a (sentences that do not contribute to the discourse structure such as photo captions, text links for images, etc.) as well as speech labels.the documents span across the domains of business, crime, disaster and politics from three major news sources that report global news and are widely used: nyt (usa), reuters (europe) and xinhua (china). we include 300 articles each (75 per domain) from reuters and xinhua that are collected by crawling the web and cover news events between 2018-'19. nyt documents are taken from existing corpora, including 102 documents from kbp 2015 4 @xcite and 100 documents (25 per domain) from the annotated nyt corpus @xcite . we trained two annotators for multiple iterations before we started the official annotations. in the beginning, each annotator completed 100 common documents (eight from each of the domains and sources and four from the kbp) within the corpus to measure annotator's agreement. the two annotators achieved cohen's κ score @xcite ) of 0.69144,0.72389 and 0.87525 for the eight finegrained, three coarse-grained and speech label annotations respectively. then, the remaining documents from each domain and news source were split evenly between the two annotators. detailed distributions of the created corpus, including distributions of different content types across domains and media sources are reported in tables 2 and 3 respectively. we find that distributions of content types vary depending on either domains or media sources. for instance, disaster documents report more consequences (m2) and anecdotal events (d2), crime documents contain more previous events (c1) and historical events (d1), while politics documents have the most opinionated contents (sentences in categories d3 and d4) immediately followed by business documents. furthermore, among different sources, nyt articles are the most opinionated and describe historical events most often, followed by reuters. in contrast, xinhua articles has relatively more sentences describing the main event. speech labels and content type labels are separately annotated and each sentence has both a content type label and a speech label (binary, speech as independent content. we used nltk @xcite to identify sentence boundaries in the body text. occasionally, one sentence is wrongly split into multiple sentences, the annotators were instructed to assign them with the same label. ## document-level neural network model for discourse profiling a wide range of computational models has been applied for extracting different forms of discourse structures. however, across several tasks, neural network methods @xcite are found the most effective, with relatively superior performance obtained by modeling discourse-level context @xcite . as an initial attempt, we use a hierarchical neural network to derive sentence representations and a document encoding, and model associations between each sentence and the main topic of the document when determining content types for sentences. shown in figure 1 , it first uses a wordlevel bi-lstm layer @xcite with soft-attention over word representations to generate intermediate sentence representations which are further enriched with the context information using another sentence-level bi-lstm. enriched sentence representations are then averaged with their soft-attention weights to generate document encoding. the final prediction layers model associations between the document encoding and each sentence encoding to predict sentence types. context-aware sentence encoding: let a document be a sequence of sentences {s 1 , s 2 ..s n }, which in turn are sequences of words {(w 11 , w 12 ..) .. (w n1 , w n2 , ..)}. we first transform a sequence of words in each sentence to contextualized word representations using elmo @xcite followed by a word-level bilstm layer to obtain their hidden state representations h s . then, we take weighted sums of hidden representations using soft-attention scores to obtain intermediate sentence encodings (s i ) that are uninformed of the contextual information. therefore, we apply another sentence-level bilstm over the sequence of sentence encodings to model interactions among sentences and smoothen context flow from the headline until the last sentence in a document. the hidden states (h t ) of the sentence-level bi-lstm are used as sentence encodings. document encoding: we generate a reference document encoding, as a weighted sum over sentence encodings using their soft-attention weights. modeling associations with the main topic: sentence types are interpreted with respect to the main event. however, while the sentence-level bilstm augments sentence representations with the local context, they may be still unaware of the main topic. therefore, we compute element-wise products and differences between the document encoding and a sentence encoding to measure their correlations, and further concatenate the products and differ- to alleviate the influence of randomness in neural model training and obtain stable experimental results, we run each neural model ten times with random seeds and report the average performance. ## conclusion we have created the first broad-coverage corpus of news articles annotated with a theoretically grounded functional discourse structure. our initial experiments using neural models ascertain the feasibility of this task. we conducted experiments and demonstrated the usefulness of news discourse profiling for event coreference resolution. in the future, we will further improve the performance of news discourse profiling by investigating subgenres of news articles, and extensively explore its usage for various other nlp tasks and applications.