Crawling, Collecting, and Condensing News Comments

Traditionally, public opinion and policy is decided by issuing surveys and performing censuses designed to measure what the public thinks about a certain topic. Within the past five years social networks such as Facebook and Twitter have gained traction for collection of public opinion about current events. Academic research on Facebook data proves difficult since the platform is generally closed. Twitter on the other hand restricts the conversation of its users making it difficult to extract large scale concepts from the microblogging infrastructure. News comments provide a rich source of discourse from individuals who are passionate about an issue. Furthermore, due to the overhead of commenting, the population of commenters is necessarily biased towards individual who have either strong opinions of a topic or in depth knowledge of the given issue. Furthermore, their comments are often a collection of insight derived from reading multiple articles on any given topic. Unfortunately the commenting systems employed by news companies are not implemented by a single entity, and are often stored and generated using AJAX, which causes traditional crawlers to ignore them. To make matters worse they are often noisy; containing spam, poor grammar, and excessive typos. Furthermore, due to the anonymity of comment systems, conversations can often be derailed by malicious users or inherent biases in the commenters. In this thesis we discuss the design and creation of a crawler designed to extract comments from domains across the internet. For practical purposes we create a semiautomatic parser generator and describe how our system attempts to employ user feedback to predict which remote procedure calls are used to load comments. By reducing comment systems into remote procedure calls, we simplify the internet into a much simpler space, where we can focus on the data, almost independently from its presentation. Thus we are able to quickly create high fidelity parsers to extract comments from a web page. Once we have our system, we show the usefulness by attempting to extract meaningful opinions from the large collections we collect. Unfortunately doing so in real time is shown to foil traditional summarization systems, which are designed to handle dozens of well formed documents. In attempting to solve this problem we create a new algorithm, KLSum+, that outperforms all its competitors in efficiency while generally scoring well against the ROUGE SU4 metric. This algorithm factors in background models to boost accuracy, but performs over 50 times faster than alternatives. Furthermore, using the summaries we see that the data collected can provide useful insight into public opinion and even provide the key points of discourse

An Overview on Language Models: Recent Developments and Outlook

Language modeling studies the probability distributions over strings of texts. It is one of the most fundamental tasks in natural language processing (NLP). It has been widely used in text generation, speech recognition, machine translation, etc. Conventional language models (CLMs) aim to predict the probability of linguistic sequences in a causal manner. In contrast, pre-trained language models (PLMs) cover broader concepts and can be used in both causal sequential modeling and fine-tuning for downstream applications. PLMs have their own training paradigms (usually self-supervised) and serve as foundation models in modern NLP systems. This overview paper provides an introduction to both CLMs and PLMs from five aspects, i.e., linguistic units, structures, training methods, evaluation methods, and applications. Furthermore, we discuss the relationship between CLMs and PLMs and shed light on the future directions of language modeling in the pre-trained era

Discourse structure and language technology

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.An increasing number of researchers and practitioners in Natural Language Engineering face the prospect of having to work with entire texts, rather than individual sentences. While it is clear that text must have useful structure, its nature may be less clear, making it more difficult to exploit in applications. This survey of work on discourse structure thus provides a primer on the bases of which discourse is structured along with some of their formal properties. It then lays out the current state-of-the-art with respect to algorithms for recognizing these different structures, and how these algorithms are currently being used in Language Technology applications. After identifying resources that should prove useful in improving algorithm performance across a range of languages, we conclude by speculating on future discourse structure-enabled technology.Peer Reviewe

Ranking, Labeling, and Summarizing Short Text in Social Media

One of the key features driving the growth and success of the Social Web is large-scale participation through user-contributed content – often through short text in social media. Unlike traditional long-form documents – e.g., Web pages, blog posts – these short text resources are typically quite brief (on the order of 100s of characters), often of a personal nature (reflecting opinions and reactions of users), and being generated at an explosive rate. Coupled with this explosion of short text in social media is the need for new methods to organize, monitor, and distill relevant information from these large-scale social systems, even in the face of the inherent “messiness” of short text, considering the wide variability in quality, style, and substance of short text generated by a legion of Social Web participants. Hence, this dissertation seeks to develop new algorithms and methods to ensure the continued growth of the Social Web by enhancing how users engage with short text in social media. Concretely, this dissertation takes a three-fold approach: First, this dissertation develops a learning-based algorithm to automatically rank short text comments associated with a Social Web object (e.g., Web document, image, video) based on the expressed preferences of the community itself, so that low-quality short text may be filtered and user attention may be focused on highly-ranked short text. Second, this dissertation organizes short text through labeling, via a graph- based framework for automatically assigning relevant labels to short text. In this way meaningful semantic descriptors may be assigned to short text for improved classification, browsing, and visualization. Third, this dissertation presents a cluster-based summarization approach for extracting high-quality viewpoints expressed in a collection of short text, while maintaining diverse viewpoints. By summarizing short text, user attention may quickly assess the aggregate viewpoints expressed in a collection of short text, without the need to scan each of possibly thousands of short text items

Condensing Information: From Supervised To Crowdsourced Learning

The main focus of this dissertation is new and improved ways of bringing high quality content to the users by leveraging the power of machine learning. Starting with a large amount of data we want to condense it into an easily digestible form by removing redundant and irrelevant parts and retaining only important information that is of interest to the user. Learning how to perform this from data allows us to use more complex models that better capture the notion of good content. Starting with supervised learning, this thesis proposes using structured prediction in conjunction with support vector machines to learn how to produce extractive summaries of textual documents. Representing summaries as a multivariate objects allows for modeling the dependencies between the summary components. An efficient approach to learning and predicting summaries is still possible by using a submodular objective/scoring function despite complex output space. The discussed approach can also be adapted to unsupervised setting and used to condense information in novel ways while retaining the same efficient submodular framework. Incorporating temporal dimension into summarization objective lead to a new way of visualizing flow of ideas and identifying novel contributions in a time-stamped corpus, which in turn help users gain a high level insight into evolution of it. Lastly, instead of trying to explicitly define an automated function used to condense information, one can leverage crowdsourcing. In particular, this thesis considers user feedback on online user-generated content to construct and improve content rankings. An analysis of a real-world dataset is presented and results suggest more accurate models of actual user voting patterns. Based on this new knowledge, an improved content ranking algorithm is proposed that delivers good content to the users in a shorter timeframe

NLP-Based Techniques for Cyber Threat Intelligence

In the digital era, threat actors employ sophisticated techniques for which, often, digital traces in the form of textual data are available. Cyber Threat Intelligence~(CTI) is related to all the solutions inherent to data collection, processing, and analysis useful to understand a threat actor's targets and attack behavior. Currently, CTI is assuming an always more crucial role in identifying and mitigating threats and enabling proactive defense strategies. In this context, NLP, an artificial intelligence branch, has emerged as a powerful tool for enhancing threat intelligence capabilities. This survey paper provides a comprehensive overview of NLP-based techniques applied in the context of threat intelligence. It begins by describing the foundational definitions and principles of CTI as a major tool for safeguarding digital assets. It then undertakes a thorough examination of NLP-based techniques for CTI data crawling from Web sources, CTI data analysis, Relation Extraction from cybersecurity data, CTI sharing and collaboration, and security threats of CTI. Finally, the challenges and limitations of NLP in threat intelligence are exhaustively examined, including data quality issues and ethical considerations. This survey draws a complete framework and serves as a valuable resource for security professionals and researchers seeking to understand the state-of-the-art NLP-based threat intelligence techniques and their potential impact on cybersecurity

Pretrained Transformers for Text Ranking: BERT and Beyond

The goal of text ranking is to generate an ordered list of texts retrieved from a corpus in response to a query. Although the most common formulation of text ranking is search, instances of the task can also be found in many natural language processing applications. This survey provides an overview of text ranking with neural network architectures known as transformers, of which BERT is the best-known example. The combination of transformers and self-supervised pretraining has been responsible for a paradigm shift in natural language processing (NLP), information retrieval (IR), and beyond. In this survey, we provide a synthesis of existing work as a single point of entry for practitioners who wish to gain a better understanding of how to apply transformers to text ranking problems and researchers who wish to pursue work in this area. We cover a wide range of modern techniques, grouped into two high-level categories: transformer models that perform reranking in multi-stage architectures and dense retrieval techniques that perform ranking directly. There are two themes that pervade our survey: techniques for handling long documents, beyond typical sentence-by-sentence processing in NLP, and techniques for addressing the tradeoff between effectiveness (i.e., result quality) and efficiency (e.g., query latency, model and index size). Although transformer architectures and pretraining techniques are recent innovations, many aspects of how they are applied to text ranking are relatively well understood and represent mature techniques. However, there remain many open research questions, and thus in addition to laying out the foundations of pretrained transformers for text ranking, this survey also attempts to prognosticate where the field is heading

VidChapters-7M: Video Chapters at Scale

Segmenting long videos into chapters enables users to quickly navigate to the information of their interest. This important topic has been understudied due to the lack of publicly released datasets. To address this issue, we present VidChapters-7M, a dataset of 817K user-chaptered videos including 7M chapters in total. VidChapters-7M is automatically created from videos online in a scalable manner by scraping user-annotated chapters and hence without any additional manual annotation. We introduce the following three tasks based on this data. First, the video chapter generation task consists of temporally segmenting the video and generating a chapter title for each segment. To further dissect the problem, we also define two variants of this task: video chapter generation given ground-truth boundaries, which requires generating a chapter title given an annotated video segment, and video chapter grounding, which requires temporally localizing a chapter given its annotated title. We benchmark both simple baselines and state-of-the-art video-language models for these three tasks. We also show that pretraining on VidChapters-7M transfers well to dense video captioning tasks in both zero-shot and finetuning settings, largely improving the state of the art on the YouCook2 and ViTT benchmarks. Finally, our experiments reveal that downstream performance scales well with the size of the pretraining dataset. Our dataset, code, and models are publicly available at https://antoyang.github.io/vidchapters.html.Comment: Accepted at NeurIPS 2023 Track on Datasets and Benchmarks; Project Webpage: https://antoyang.github.io/vidchapters.html ; 31 pages; 8 figure