--- license: apache-2.0 task_categories: - text-classification - table-question-answering - summarization language: - en tags: - news - articles - year:1987 pretty_name: reuters size_categories: - 10K Reuters-21578 text categorization test collection > > Distribution 1.0 (v 1.2) > 26 September 1997 > > David D. Lewis > > AT&T Labs - Research > > lewis@research.att.com > ## Profile The dataset was processed as part of our work on the [reuters-search-engine](https://github.com/IsmaelMousa/reuters-search-engine) project, where it was my primary responsibility. - **Size**: 18,594 articles. - **Fields**: 16 fields. - **Tasks**: information retrieval, natural language processing. Table 1: Description of each field in the dataset: - **New** indicates that this feature was generated through data mining and feature-engineering. - **Updated** indicates that this feature already existed but was refined, cleaned, and transformed to improve data quality. | Column / Property | Description | New | Updated | |:----------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------|:-----:|:-------:| | **`date`** | Timestamp indicating when the Reuters article was published. Stored as `datetime64[ns]`. | False | True | | **`topics`** | List of topical tags or subject categories assigned to the article (e.g., economic themes, commodities, industries). | False | True | | **`places`** | List of geographic place tags related to the article’s content, often countries or regions mentioned or relevant to the story. | False | True | | **`people`** | List of individuals referenced in the article, usually named stakeholders, analysts, officials, or quoted experts. | False | True | | **`organizations`** | List of organizations mentioned in the text, such as agencies, companies, government bodies, or institutions. | False | True | | **`exchanges`** | List of financial exchanges or markets referenced in the article (often empty when not applicable). | False | False | | **`title`** | The headline of the news article as published by Reuters. | False | True | | **`text`** | Full body text of the article, containing the narrative, quotes, and analysis. | False | True | | **`keyword`** | A high-level category label (e.g., “business-news”) representing the main topic class of the article. | True | False | | **`domain`** | A broader thematic domain such as “food-and-drink,” grouping articles into content verticals. | True | False | | **`type`** | The type or format of the entry (e.g., “news”). Indicates the nature of the document. | True | False | | **`quality`** | Represents the structural and grammatical quality of the article’s text body. This reflects how well-formed or clean the text in the `text` field is. | True | False | | **`geographic_information`** | Structured dictionary containing geolocation metadata extracted for the article (e.g., city, state, country, coordinates, ISO codes). | True | False | | **`identities`** | List of identity-related descriptors, typically nationalities, religious groups, or political affiliations referenced in the text. | True | False | | **`title_embedding`** | Embeddings vector generated from the news title reflecting the semantic meaning of its core intent. | True | False | | **`text_embedding`** | Embeddings vector generated from the news body text reflecting the semantic meaning of its content. | True | False | ## Methodology This work follows an Extract–Transform–Load (ETL) pipeline to produce a structured and retrieval-ready news dataset. All processing steps were implemented in Python and executed with A100 GPU acceleration. - **Data Preprocessing** The raw dataset was cleaned using `Pandas` and `BeautifulSoup`. Non-essential metadata fields were removed, duplicate articles were eliminated based on body text, and records with missing content were discarded. Text fields were normalized by stripping whitespace, capitalizing sentences, and removing HTML artifacts. Publication dates were parsed and standardized to ISO 8601 format. - **Semantic Clustering** Document-level semantic representations were generated using the `Sentence-Transformers` library with the *thenlper/gte-base* model. The resulting embeddings were clustered using `K-means` from `scikit-learn` into seven predefined topical groups, each mapped to a high-level news category. - **Automated Classification** Domain, content type, and quality labels were inferred using `NVIDIA NeMo Curator` classifiers. Distributed processing was performed via `Ray`, and data exchange was handled using JSONL readers and writers. To improve memory efficiency, the dataset was split into partitions and processed sequentially. - **Named Entity and Geographic Enrichment** Named entities were extracted from article text and datelines using `spaCy (en_core_web_lg)`. Entities were normalized and categorized into organizations, people, places, and identities. Dateline locations were resolved into structured geographic metadata, including latitude and longitude, using `Geopy` with the *Nominatim* geocoder. - **Dense Vector Encoding** Dense embeddings for article titles and full text were generated using the `Sentence-Transformers` framework with the *google/embeddinggemma-300m* model. These embeddings enable semantic similarity search and neural information retrieval. - **Postprocessing** Classifier outputs were normalized and renamed, missing dates were backfilled from datelines, and intermediate fields were removed. Documents lacking geographic information were filtered out. ## Usage ```python from datasets import load_dataset reuters = load_dataset("IsmaelMousa/reuters", split="train") print(reuters["text"][0]) ``` output: ```python Plm cos inc said its plm power co unit broke off merger discussions with sunlaw energy corp of beverly hills, calif. in january plm power entered into a letter of intent to negotiate a potential acquisition of sunlaw, subject to substantial due diligence, the company said. but it also said the two companies were not able to agree on mutually satisfactory final terms and conditions. ``` ## License The rights to the reuters are reserved by their respective authors. This dataset is provided under the Apache 2.0 license for both personal and commercial use, with proper attribution.