Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model

• URL: http://arxiv.org/abs/2004.06793v1
• Date: Tue, 14 Apr 2020 20:18:21 GMT
• Title: Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model
• Authors: Toktam A. Oghaz, Ece C. Mutlu, Jasser Jasser, Niloofar Yousefi, Ivan Garibay
• Abstract summary: We propose a novel event-based narrative summary extraction framework. Our framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. We evaluate our model on a large corpus of Twitter data, including more than one million tweets in the domain of the disinformation campaigns conducted against the White Helmets of Syria.
• Score: 4.073849137967964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online social media platforms are turning into the prime source of news and narratives about worldwide events. However,a systematic summarization-based narrative extraction that can facilitate communicating the main underlying events is lacking. To address this issue, we propose a novel event-based narrative summary extraction framework. Our proposed framework is designed as a probabilistic topic model, with categorical time distribution, followed by extractive text summarization. Our topic model identifies topics' recurrence over time with a varying time resolution. This framework not only captures the topic distributions from the data, but also approximates the user activity fluctuations over time. Furthermore, we define significance-dispersity trade-off (SDT) as a comparison measure to identify the topic with the highest lifetime attractiveness in a timestamped corpus. We evaluate our model on a large corpus of Twitter data, including more than one million tweets in the domain of the disinformation campaigns conducted against the White Helmets of Syria. Our results indicate that the proposed framework is effective in identifying topical trends, as well as extracting narrative summaries from text corpus with timestamped data.

Related papers

• MomentDiff: Generative Video Moment Retrieval from Random to Real [71.40038773943638]
  We provide a generative diffusion-based framework called MomentDiff. MomentDiff simulates a typical human retrieval process from random browsing to gradual localization. We show that MomentDiff consistently outperforms state-of-the-art methods on three public benchmarks.
  arXiv  Detail & Related papers  (2023-07-06T09:12:13Z)
• Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological Order [78.46986998674181]
  We propose a Unified Timeline Summarizer (UTS) that can generate abstractive and extractive timeline summaries in time order. We augment the previous Chinese large-scale timeline summarization dataset and collect a new English timeline dataset. UTS achieves state-of-the-art performance in terms of both automatic and human evaluations.
  arXiv  Detail & Related papers  (2023-01-02T20:29:40Z)
• A Closer Look at Debiased Temporal Sentence Grounding in Videos: Dataset, Metric, and Approach [53.727460222955266]
  Temporal Sentence Grounding in Videos (TSGV) aims to ground a natural language sentence in an untrimmed video. Recent studies have found that current benchmark datasets may have obvious moment annotation biases. We introduce a new evaluation metric "dR@n,IoU@m" that discounts the basic recall scores to alleviate the inflating evaluation caused by biased datasets.
  arXiv  Detail & Related papers  (2022-03-10T08:58:18Z)
• Topic-Aware Encoding for Extractive Summarization [15.113768658584979]
  We propose a topic-aware encoding for document summarization to deal with this issue. A neural topic model is added in the neural-based sentence-level representation learning to adequately consider the central topic information. The experimental results on three public datasets show that our model outperforms the state-of-the-art models.
  arXiv  Detail & Related papers  (2021-12-17T15:26:37Z)
• TopNet: Learning from Neural Topic Model to Generate Long Stories [43.5564336855688]
  Long story generation (LSG) is one of the coveted goals in natural language processing. We propose emphTopNet to obtain high-quality skeleton words to complement the short input. Our proposed framework is highly effective in skeleton word selection and significantly outperforms state-of-the-art models in both automatic evaluation and human evaluation.
  arXiv  Detail & Related papers  (2021-12-14T09:47:53Z)
• HTMOT : Hierarchical Topic Modelling Over Time [0.0]
  We propose a novel method, HTMOT, to perform Hierarchical Topic Modelling Over Time. We show that only applying time modelling to deep sub-topics provides a way to extract specific stories or events. Our results show that our training procedure is fast and can extract accurate high-level topics and temporally precise sub-topics.
  arXiv  Detail & Related papers  (2021-11-22T11:02:35Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Modeling Collective Anticipation and Response on Wikipedia [1.299941371793082]
  We propose a model that describes the dynamics around peaks of popularity by incorporating key features, i.e., the anticipatory growth and the decay of collective attention together with circadian rhythms. Our work demonstrates the importance of appropriately modeling all phases of collective attention, as well as the connection between temporal patterns of attention and characteristic underlying information of the events they represent.
  arXiv  Detail & Related papers  (2021-05-23T09:51:32Z)
• Bengali Abstractive News Summarization(BANS): A Neural Attention Approach [0.8793721044482612]
  We present a seq2seq based Long Short-Term Memory (LSTM) network model with attention at encoder-decoder. Our proposed system deploys a local attention-based model that produces a long sequence of words with lucid and human-like generated sentences. We also prepared a dataset of more than 19k articles and corresponding human-written summaries collected from bangla.bdnews24.com1.
  arXiv  Detail & Related papers  (2020-12-03T08:17:31Z)
• Topic Adaptation and Prototype Encoding for Few-Shot Visual Storytelling [81.33107307509718]
  We propose a topic adaptive storyteller to model the ability of inter-topic generalization. We also propose a prototype encoding structure to model the ability of intra-topic derivation. Experimental results show that topic adaptation and prototype encoding structure mutually bring benefit to the few-shot model.
  arXiv  Detail & Related papers  (2020-08-11T03:55:11Z)
• Screenplay Summarization Using Latent Narrative Structure [78.45316339164133]
  We propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models. We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays. Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode.
  arXiv  Detail & Related papers  (2020-04-27T11:54:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.