Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles

• URL: http://arxiv.org/abs/2010.15067v1
• Date: Wed, 28 Oct 2020 16:20:05 GMT
• Title: Graph-based Topic Extraction from Vector Embeddings of Text Documents: Application to a Corpus of News Articles
• Authors: M. Tarik Altuncu, Sophia N. Yaliraki, Mauricio Barahona
• Abstract summary: We present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning. We show the advantages of graph-based clustering through end-to-end comparisons with other popular clustering and topic modelling methods. This work is showcased through an analysis of a corpus of US news coverage during the presidential election year of 2016.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Production of news content is growing at an astonishing rate. To help manage and monitor the sheer amount of text, there is an increasing need to develop efficient methods that can provide insights into emerging content areas, and stratify unstructured corpora of text into `topics' that stem intrinsically from content similarity. Here we present an unsupervised framework that brings together powerful vector embeddings from natural language processing with tools from multiscale graph partitioning that can reveal natural partitions at different resolutions without making a priori assumptions about the number of clusters in the corpus. We show the advantages of graph-based clustering through end-to-end comparisons with other popular clustering and topic modelling methods, and also evaluate different text vector embeddings, from classic Bag-of-Words to Doc2Vec to the recent transformers based model Bert. This comparative work is showcased through an analysis of a corpus of US news coverage during the presidential election year of 2016.

Related papers

• CAST: Corpus-Aware Self-similarity Enhanced Topic modelling [16.562349140796115]
  We introduce CAST: Corpus-Aware Self-similarity Enhanced Topic modelling, a novel topic modelling method. We find self-similarity to be an effective metric to prevent functional words from acting as candidate topic words. Our approach significantly enhances the coherence and diversity of generated topics, as well as the topic model's ability to handle noisy data.
  arXiv  Detail & Related papers  (2024-10-19T15:27:11Z)
• From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions [63.11097464396147]
  We introduce a novel benchmark YTSeg focusing on spoken content that is inherently more unstructured and both topically and structurally diverse. We also introduce an efficient hierarchical segmentation model MiniSeg, that outperforms state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-02-27T15:59:37Z)
• Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation [71.40119152422295]
  We propose a lightweight, scalable and generalizable approach to identify text reading order. The model is language-agnostic and runs effectively across multi-language datasets. It is small enough to be deployed on virtually any platform including mobile devices.
  arXiv  Detail & Related papers  (2023-05-04T06:21:00Z)
• SpaText: Spatio-Textual Representation for Controllable Image Generation [61.89548017729586]
  SpaText is a new method for text-to-image generation using open-vocabulary scene control. In addition to a global text prompt that describes the entire scene, the user provides a segmentation map. We show its effectiveness on two state-of-the-art diffusion models: pixel-based and latent-conditional-based.
  arXiv  Detail & Related papers  (2022-11-25T18:59:10Z)
• Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddings [46.324584649014284]
  A topic model is often formulated as a generative model that explains how each word of a document is generated given a set of topics and document-specific topic proportions. This paper introduces a new topic-modeling framework where each document is viewed as a set of word embedding vectors and each topic is modeled as an embedding vector in the same embedding space. Embedding the words and topics in the same vector space, we define a method to measure the semantic difference between the embedding vectors of the words of a document and these of the topics, and optimize the topic embeddings to minimize the expected difference over all documents.
  arXiv  Detail & Related papers  (2022-03-03T08:46:23Z)
• 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)
• BASS: Boosting Abstractive Summarization with Unified Semantic Graph [49.48925904426591]
  BASS is a framework for Boosting Abstractive Summarization based on a unified Semantic graph. A graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.
  arXiv  Detail & Related papers  (2021-05-25T16:20:48Z)
• Topical Change Detection in Documents via Embeddings of Long Sequences [4.13878392637062]
  We formulate the task of text segmentation as an independent supervised prediction task. By fine-tuning on paragraphs of similar sections, we are able to show that learned features encode topic information. Unlike previous approaches, which mostly operate on sentence-level, we consistently use a broader context.
  arXiv  Detail & Related papers  (2020-12-07T12:09:37Z)
• The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks [19.099852869845495]
  Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks. We focus on the problem of textual similarity from two perspectives: matching documents on a granular level, and an abstract level. We empirically demonstrate, across two datasets from different domains, that despite high performance in abstract document matching as expected, contextual embeddings are consistently (and at times, vastly) outperformed by simple baselines like TF-IDF for more granular tasks.
  arXiv  Detail & Related papers  (2020-11-02T18:41:32Z)
• Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation [50.01708049531156]
  We focus on a new practical task, document-scale text content manipulation, which is the opposite of text style transfer. In detail, the input is a set of structured records and a reference text for describing another recordset. The output is a summary that accurately describes the partial content in the source recordset with the same writing style of the reference.
  arXiv  Detail & Related papers  (2020-02-24T12:52:10Z)
• Two-Level Transformer and Auxiliary Coherence Modeling for Improved Text Segmentation [9.416757363901295]
  We introduce a novel supervised model for text segmentation with simple but explicit coherence modeling. Our model -- a neural architecture consisting of two hierarchically connected Transformer networks -- is a multi-task learning model that couples the sentence-level segmentation objective with the coherence objective that differentiates correct sequences of sentences from corrupt ones.
  arXiv  Detail & Related papers  (2020-01-03T17:06:41Z)

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.