Abstract

In order to expand their reach and increase website ad revenue, media outlets have started using clickbait techniques to lure readers to click on articles on their digital platform. Having successfully enticed the user to open the article, the article fails to satiate his curiosity serving only to boost click-through rates. Initial methods for this task were dependent on feature engineering, which varies with each dataset. Industry systems have relied on an exhaustive set of rules to get the job done. Neural networks have barely been explored to perform this task. We propose a novel approach considering different textual embeddings of a news headline and the related article. We generate sub-word level embeddings of the title using Convolutional Neural Networks and use them to train a bidirectional LSTM architecture. An attention layer allows for calculation of significance of each term towards the nature of the post. We also generate Doc2Vec embeddings of the title and article text and model how they interact, following which it is concatenated with the output of the previous component. Finally, this representation is passed through a neural network to obtain a score for the headline. We test our model over 2538 posts (having trained it on 17000 records) and achieve an accuracy of 83.49% outscoring previous state-of-the-art approaches.

Abstract

With news stories coming from a variety of sources, it is crucial for news aggregators to present interesting articles to the user to maximize their engagement. This creates the need for a news recommendation system which understands the content of the articles as well as accounts for the users’ preferences. Methods such as Collaborative Filtering, which are well known for general recommendations, are not suitable for news because of the short life span of articles and because of the large number of articles published each day. Apart from this, such methods do not harness the information present in the sequence in which the articles are read by the user and hence are unable to account for the specific and generic interests of the user which may keep changing with time. In order to address these issues for news recommendation, we propose the Recurrent Attentive Recommendation Engine (RARE). RARE consists of two components and utilizes the distributed representations of news articles. The first component is used to model the user’s sequential behaviour of news reading in order to understand her general interests, i.e., to get a summary of her interests. The second component utilizes an article level attention mechanism to understand her specific interests. We feed the information obtained from both the components to a Siamese Network in order to make predictions which pertain to the user’s generic as well as specific interests. We carry out extensive experiments over three real-world datasets and show that RARE outperforms the state-of-the-art. Furthermore, we also demonstrate the effectiveness of our method in handling the cold start cases.

Abstract

Bizarre news items are those news items so strange and unusual that readers might question the claims presented in the news. This paper presents the first machine learning approach to bizarre news detection in online news media. We contribute by compiling the first bizarre news corpus of 23754 bizarre news headlines, and by developing a bizarre news detection model based on various syntactic and semantic features. We experiment with various machine learning techniques including deep learning methods, and find that a logistic regression classifier achieves an F1 score of 0.92 for the task.

Abstract

Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art in ADR mention extraction uses Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a multi-task learning based method which can utilize a similar auxiliary task (adverse drug event detection) to enhance the performance of the main task, i.e., ADR extraction. Furthermore, in absence of the auxiliary task dataset, we propose a novel joint multi-task learning method to automatically generate weak supervision dataset for the auxiliary task when a large pool of unlabeled tweets is available. Experiments with ∼0.48M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by ∼7.2% in terms of F1 score.

Abstract

Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time, much before any official reporting. Current state-of-the-art methods in ADR mention extraction use Recurrent Neural Networks (RNN), which typically need large labeled corpora. Towards this end, we propose a semi-supervised method based on co-training which can exploit a large pool of unlabeled tweets to augment the limited supervised training data, and as a result enhance the performance. Experiments with ∼0.1M tweets show that the proposed approach outperforms the state-of-the-art methods for the ADR mention extraction task by ∼5% in terms of F1 score.

Abstract

Popular methods like collaborative filtering and content-based filtering have their own disadvantages. The former method requires a considerable amount of user data before making predictions, while the latter, suffers from over-specialization. In this work, we address both of these issues by coming up with a hybrid approach based on neural networks for news recommendation. The hybrid approach incorporates for both (1) user-item interaction and (2) content-information of the articles read by the user in the past. We first come up with an article-embedding based profile for the user. We then use this user profile with adequate positive and negative samples in order to train the neural network based model. The resulting model is then applied on a real-world dataset. We compare it with a set of established baselines and the experimental results show that our model outperforms the stateof-the-art.

Abstract

Text segmentation plays an important role in various Natural Language Processing (NLP) tasks like summarization, context understanding, document indexing and document noise removal. Previous methods for this task require manual feature engineering, huge memory requirements and large execution times. To the best of our knowledge, this paper is the first one to present a novel supervised neural approach for text segmentation. Specifically, we propose an attention-based bidirectional LSTM model where sentence embeddings are learned using CNNs and the segments are predicted based on contextual information. This model can automatically handle variable sized context information. Compared to the existing competitive baselines, the proposed model shows a performance improvement of ∼7% in WinDiff score on three benchmark datasets.

Abstract

Automated multi-document extractive text summarization is a widely studied research problem in the field of natural language understanding. Such extractive mechanisms compute in some form the worthiness of a sentence to be included into the summary. While the conventional approaches rely on human crafted document-independent features to generate a summary, we develop a data-driven novel summary system called HNet, which exploits the various semantic and compositional aspects latent in a sentence to capture document independent features. The network learns sentence representation in a way that, salient sentences are closer in the vector space than non-salient sentences. This semantic and compositional feature vector is then concatenated with the document dependent features for sentence ranking. Experiments on the DUC benchmark datasets (DUC-2001, DUC-2002 and DUC2004) indicate that our model shows significant performance gain of around 1.5-2 points in terms of ROUGE score compared with the state-of-the-art baselines.

Abstract

The need for cross-lingual information access is more than ever with the easy accessibility to the Internet, especially in vastly multilingual societies like India. Cross-lingual summarization can help minimize human effort needed for achieving publishable articles in multiple languages, while making the most important information available in target language in the form of summaries. We describe a flexible, web-based tool for human editing of cross-lingual summaries to rapidly generate publishable summaries in a number of Indian Languages for news articles originally published in English, and simultaneously collect detailed logs about the process, at both article and sentence level. Similar to translation post-editing logs, such logs can be used to evaluate the automated cross-lingual summaries, in terms of effort needed to make them publishable. The generated summaries along with the logs can be used to train and improve the automatic system over time.

Abstract

Social media is an useful platform to share health-related information due to its vast reach. Œis makes it a good candidate for public health monitoring tasks, specifically for pharma covigilance. We study the problem of extraction of Adverse-Drug-Reaction (ADR) mentions from social media, particularly from Twiter. Medical information extraction from social media is challenging, mainly due to short and highly informal nature of text, as compared to more technical and formal medical reports. Current methods in ADR mention extraction rely on supervised learning methods, which suffer from labeled data scarcity problem. The State-of-the-art method uses deep neural networks, specifically a class of Recurrent Neural Network (RNN) which are LongShort-Term-Memory networks (LSTMs) [6]. Deep neural networks,due to their large number of free parameters relies heavily on large annotated corpora for learning the end task. But in the real-world, it is hard to get large labeled data, mainly due to the heavy cost associated with the manual annotation. To this end, we propose a novel semi-supervised learning based RNN model, which can leverage unlabeled data also present in abundance on social media. through experiments we demonstrate the effectiveness of our method, achieving state-of-the-art performance in ADR mention extraction.