Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents

• URL: http://arxiv.org/abs/2301.00152v1
• Date: Sat, 31 Dec 2022 08:40:08 GMT
• Title: Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents
• Authors: Sayar Ghosh Roy, Anshul Padhi, Risubh Jain, Manish Gupta, Vasudeva Varma
• Abstract summary: We introduce the task of proactively forecasting popularities of sentences within online news documents. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task.
• Score: 13.537665342333488
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiple studies have focused on predicting the prospective popularity of an online document as a whole, without paying attention to the contributions of its individual parts. We introduce the task of proactively forecasting popularities of sentences within online news documents solely utilizing their natural language content. We model sentence-specific popularity forecasting as a sequence regression task. For training our models, we curate InfoPop, the first dataset containing popularity labels for over 1.7 million sentences from over 50,000 online news documents. To the best of our knowledge, this is the first dataset automatically created using streams of incoming search engine queries to generate sentence-level popularity annotations. We propose a novel transfer learning approach involving sentence salience prediction as an auxiliary task. Our proposed technique coupled with a BERT-based neural model exceeds nDCG values of 0.8 for proactive sentence-specific popularity forecasting. Notably, our study presents a non-trivial takeaway: though popularity and salience are different concepts, transfer learning from salience prediction enhances popularity forecasting. We release InfoPop and make our code publicly available: https://github.com/sayarghoshroy/InfoPopularity

Related papers

• Posterior Sampling via Autoregressive Generation [11.713451719120707]
  We propose a new framework for learning bandit algorithms from massive historical data. We use historical data to pretrain an autoregressive model to predict a sequence of repeated feedback/rewards. At decision-time, we autoregressively sample (impute) an imagined sequence of rewards for each action, and choose the action with the largest average imputed reward.
  arXiv  Detail & Related papers  (2024-05-29T19:24:44Z)
• PopALM: Popularity-Aligned Language Models for Social Media Trendy Response Prediction [6.979995957338177]
  We study trendy response prediction to automatically generate top-liked user replies to social media events. We propose Popularity-Aligned Language Models (PopALM) to distinguish responses liked by a larger audience through reinforcement learning. In experiments, we build a large-scale Weibo dataset for trendy response prediction, and its results show that PopALM can help boost the performance of advanced language models.
  arXiv  Detail & Related papers  (2024-02-29T08:28:04Z)
• Exploring Prompt-Based Methods for Zero-Shot Hypernym Prediction with Large Language Models [0.0]
  This article investigates a zero-shot approach to hypernymy prediction using large language models (LLMs) Experiments demonstrate a strong correlation between the effectiveness of language model prompts and classic patterns. We also explore prompts for predicting co-hyponyms and improving hypernymy predictions by augmenting prompts with additional information.
  arXiv  Detail & Related papers  (2024-01-09T12:13:55Z)
• Decoding the Silent Majority: Inducing Belief Augmented Social Graph with Large Language Model for Response Forecasting [74.68371461260946]
  SocialSense is a framework that induces a belief-centered graph on top of an existent social network, along with graph-based propagation to capture social dynamics. Our method surpasses existing state-of-the-art in experimental evaluations for both zero-shot and supervised settings.
  arXiv  Detail & Related papers  (2023-10-20T06:17:02Z)
• AutoCast++: Enhancing World Event Prediction with Zero-shot Ranking-based Context Retrieval [9.357912396498142]
  We introduce AutoCast++, a zero-shot ranking-based context retrieval system. Our approach first re-ranks articles based on zero-shot question-passage relevance, honing in on semantically pertinent news. We conduct both the relevance evaluation and article summarization without needing domain-specific training.
  arXiv  Detail & Related papers  (2023-10-03T08:34:44Z)
• ASPEST: Bridging the Gap Between Active Learning and Selective Prediction [56.001808843574395]
  Selective prediction aims to learn a reliable model that abstains from making predictions when uncertain. Active learning aims to lower the overall labeling effort, and hence human dependence, by querying the most informative examples. In this work, we introduce a new learning paradigm, active selective prediction, which aims to query more informative samples from the shifted target domain.
  arXiv  Detail & Related papers  (2023-04-07T23:51:07Z)
• Content Popularity Prediction in Fog-RANs: A Clustered Federated Learning Based Approach [66.31587753595291]
  We propose a novel mobility-aware popularity prediction policy, which integrates content popularities in terms of local users and mobile users. For local users, the content popularity is predicted by learning the hidden representations of local users and contents. For mobile users, the content popularity is predicted via user preference learning.
  arXiv  Detail & Related papers  (2022-06-13T03:34:00Z)
• Progressive End-to-End Object Detection in Crowded Scenes [96.92416613336096]
  Previous query-based detectors suffer from two drawbacks: first, multiple predictions will be inferred for a single object, typically in crowded scenes; second, the performance saturates as the depth of the decoding stage increases. We propose a progressive predicting method to address the above issues. Specifically, we first select accepted queries to generate true positive predictions, then refine the rest noisy queries according to the previously accepted predictions. Experiments show that our method can significantly boost the performance of query-based detectors in crowded scenes.
  arXiv  Detail & Related papers  (2022-03-15T06:12:00Z)
• Ranking Creative Language Characteristics in Small Data Scenarios [52.00161818003478]
  We adapt the DirectRanker to provide a new deep model for ranking creative language with small data. Our experiments with sparse training data show that while the performance of standard neural ranking approaches collapses with small datasets, DirectRanker remains effective.
  arXiv  Detail & Related papers  (2020-10-23T18:57:47Z)
• Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities [56.1344233010643]
  Several features are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. This study aims to predict dropout early-on, from the first week, by comparing several machine-learning approaches.
  arXiv  Detail & Related papers  (2020-08-12T10:44:49Z)
• On the Limits to Multi-Modal Popularity Prediction on Instagram -- A New Robust, Efficient and Explainable Baseline [5.859055059050023]
  We present a robust, efficient, and explainable baseline for population-based popularity prediction. We employ the latest methods in computer vision to maximize the information extracted from the visual modality. Our strongest models inform a lower limit to population-based predictability of popularity on Instagram.
  arXiv  Detail & Related papers  (2020-04-26T21:21:50Z)

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.