大規模要約資源としてのNew York Times Annotated Corpus

By 悠太菊池, 亮彦渡邉, 大也高村, 学奥村. 2015

Subfields: Natural Language Processing, Evaluation & Benchmarks

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Citations in the corpus

A Deep Reinforced Model for Abstractive Summarization (2017, methodology): “Further differences in the nature of the summaries and the level of complexity and abstraction between these datasets could also explain these intra-attention results, as well as the absolute ROUGE score differences between CNN/Daily Mail and NYT results.”
CTRL: A Conditional Transformer Language Model for Controllable Generation (2019, background): “…Project Gutenberg 1 , submissions from 45 subreddits, OpenWebText 2 , a large collection of news data (Hermann et al., 2015; Barrault et al., 2019; Sandhaus, 2008; Grusky et al., 2018), Amazon Reviews (McAuley et al., 2015), Europarl and UN data from WMT (En-De, En-Es, En-Fr) (Barrault et al.,…”
Fine-tune BERT for Extractive Summarization (2019, methodology): “We evaluated on two benchmark datasets, namely the CNN/DailyMail news highlights dataset (Hermann et al., 2015) and the New York Times Annotated Corpus (NYT; Sandhaus 2008).”
MLSUM: The Multilingual Summarization Corpus (2020, background): “New York Times Corpus This large corpus for summarization consists of hundreds of thousand of articles from The New York Times(Sandhaus, 2008), spanning over 20 years.”
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (2018, methodology): “Table 1 compares XSum with the CNN, DailyMail, and NY Times benchmarks.”
On Faithfulness and Factuality in Abstractive Summarization (2020, background): “We choose to focus our study on extreme summarization for the following reasons: First, this task aims to create a single-sentence summary of a news article; these shorter summaries are relatively easier to annotate and analyze than longer summaries such as story highlights from the CNN/Dailymail da”
Bottom-Up Abstractive Summarization (2018, background): “, 2016a), and the NYT corpus (Sandhaus, 2008), which are both standard corpora for news summarization.”
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies (2018, background): “The New York Times Annotated Corpus (Sand-haus, 2008) is the largest summarization dataset currently available.”
Text Summarization with Pretrained Encoders (2019, methodology): “We evaluated our model on three benchmark datasets, namely the CNN/DailyMail news highlights dataset (Hermann et al., 2015), the New York Times Annotated Corpus (NYT; Sandhaus 2008), and XSum (Narayan et al., 2018a).”
Word embeddings quantify 100 years of gender and ethnic stereotypes (2017, methodology): “We train embeddings over time from The New York Times Annotated Corpus (28), using 1.8 million articles from the New York Times between 1988 and 2005.”
Evaluating the Factual Consistency of Abstractive Text Summarization (2019, background): “, 2018), and availability of large-scale supervised datasets (Sandhaus, 2008; Nallapati et al., 2016; Grusky et al., 2018; Narayan et al., 2018; Sharma et al., 2019) allowed deep learningbased approaches to dominate the field.”
Neural Text Summarization: A Critical Evaluation (2019, background): “NYT (Sandhaus, 2008) is a collection of articles from the New York Times magazine with abstracts written by library scientists.”
Understanding the Origins of Bias in Word Embeddings (2018, methodology): “We demonstrate our technique through experimental results, initially on a simplified corpus of Wikipedia articles in broad use [20], and then on a New York Times corpus [18].”
Posterior calibration and exploratory analysis for natural language processing models (2015, methodology): “We tag and parse a 193,403 article subset of the Annotated New York Times LDC corpus (Sandhaus, 2008), which includes articles about world”
Injecting Logical Background Knowledge into Embeddings for Relation Extraction (2015, methodology): “Distant Supervision Evaluation We follow the procedure as used in Riedel et al. (2013) for evaluating knowledge base completion of Freebase (Bol-lacker et al., 2008) with textual data from the NY-Times corpus (Sandhaus, 2008).”
Deep Communicating Agents for Abstractive Summarization (2018, methodology): “NYT dataset (Sandhaus, 2008) is a collection of articles published between 1996 and 2007.”
Learning-Based Single-Document Summarization with Compression and Anaphoricity Constraints (2016, methodology): “We focus our evaluation on the New York Times Annotated corpus (Sandhaus, 2008).”
BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization (2019, background): “tion datasets have been explored so far (Sandhaus, 2008; Napoles et al., 2012; Hermann et al., 2015; Grusky et al., 2018).”