DUC in context

By Paul Over, Hoa Trang Dang, Donna K. Harman. 2007

205 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Dialogue & Conversational AI

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Citations in the corpus

A Neural Attention Model for Abstractive Sentence Summarization (2015, methodology): “The standard sentence summarization evaluation set is associated with the DUC-2003 and DUC-2004 shared tasks (Over et al., 2007).”
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks (2016, methodology): “The task of abstractive sentence summarization was later formalized around the DUC-2003 and DUC-2004 competitions (Over et al., 2007), where the T OP - IARY system (Zajic et al., 2004) was the state-of-the-art.”
Abstractive Summarization of Reddit Posts with Multi-level Memory Networks (2018, background): “The source text of most datasets (Over et al., 2007; Hermann et al., 2015; Cohan et al., 2018; Grusky et al., 2018; Narayan et al., 2018a) originates from formal documents such as news articles, which have some structural patterns of which extractive methods better take advantage.”
Cutting-off Redundant Repeating Generations for Neural Abstractive Summarization (2016, background): “The very short ABS task in DUC-2003 and 2004 Over et al. (2007) is a typical example because it requires the generation of a summary in a pre-deﬁned limited output space, such as ten words or 75 bytes.”
Improving Abstraction in Text Summarization (2018, methodology): “The Gigaword (Graff and Cieri, 2003) and some DUC datasets (Over et al., 2007) have been used for headline generation models (Rush et al., 2015; Nallapati et al., 2016), where the generated summary is shorter than 75 characters.”
Neural Headline Generation on Abstract Meaning Representation (2016, methodology): “The development data is DUC-2003 data, and test data are both DUC-2004 (Over et al., 2007) and sentence-headline pairs obtained from the annotated Gigaword corpus as well as training data 5 .”
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization (2018, background): “DUC-2004 corpus for tasks 1 & 2 (Over et al., 2007) consists of 500 documents.”
SUM-QE: a BERT-based Summary Quality Estimation Model (2019, methodology): “We use datasets from the NIST DUC-05, DUC06 and DUC-07 shared tasks (Dang, 2006a,b; Over et al., 2007).”
Efficient Summarization with Read-Again and Copy Mechanism (2016, methodology): “Similar to Rush et al. (2015), we train our neural model on the Gigaword training set, and show the models’ performances on DUC2004.”
SEQˆ3: Differentiable Sequence-to-Sequence-to-Sequence Autoencoder for Unsupervised Abstractive Sentence Compression (2019, methodology): “We also test (without retraining) SEQ3 on DUC-2003 and DUC-2004 shared tasks (Over et al., 2007), containing 624/500 news articles each, paired with 4 reference summaries capped at 75 bytes.”
Re-evaluating Automatic Summarization with BLEU and 192 Shades of ROUGE (2015, methodology): “In D UC -2004 (Over et al., 2007), human annotations used to compute summary coverage are carried out by identiﬁcation of matching peer units (PUs), the units in a peer summary that express content of the corresponding model summary.”
How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature (2019, methodology): “1 This practice likely followed the DUC shared task, which called for summaries of length ﬁxed in words or bytes (Over et al., 2007) or inﬂuential work advocating for ﬁxed summary length around 85-90 words (Gold-stein et al., 1999).”
Improving the Estimation of Word Importance for News Multi-Document Summarization - Extended Technical Report (2016, methodology): “We carry out our experiments on two datasets from the Document Understanding Conference (DUC) (Over et al., 2007).”
How2: A Large-scale Dataset for Multimodal Language Understanding (2018, methodology): “ ViaVoice [31] EN 3 3(V) 34.9 hours AV-ASR LRW [32] EN 3 3(V) 800 hours STT Fisher [33] EN, ES 3 7 150 hours STT Audiobooks [34] EN, FR 3 7 236 hours SUM CNN/DMC [35, 36] EN 7 7 286,817 pairs SUM DUC [37] EN 7 7 500 pairs SUM [38] EN CZ 3 3(V) 492,402 pairs is otherwise often performed using Fisher-”
Automatic Text Summarization (2008, methodology): “Some were about evaluation of summarization using relevance prediction [1], ROUGEeval package [2], SUMMAC, NTCIR, and DUC [3] and voted regression model [4].”
Disentangling the Properties of Human Evaluation Methods: A Classification System to Support Comparability, Meta-Evaluation and Reproducibility Testing (2020, result): “…always gauged the trustworthiness of automatic evaluation metrics in terms of how well, and how consistently, they correlate with human evaluation scores (Over et al., 2007; Gatt and Belz, 2008; Bojar et al., 2016; Shi-morina et al., 2018; Ma et al., 2019; Mille et al., 2019; Duˇsek et al., 2020).”
Global Voices: Crossing Borders in Automatic News Summarization (2019, background): “In comparison, the DUC-2004 dataset (Over et al., 2007) only provides subtle format suggestions and leaves the summary contents almost entirely to the decisions of the writers: “...Imagine that to save time, rather than read through a set of complete documents, you first read a list of very short…”
RNN-based Encoder-decoder Approach with Word Frequency Estimation (2017, background): “The very short ABS task in DUC-2003 and 2004 Over et al. [2007] is a typical example because it requires the generation of a summary in a pre-deﬁned limited output space, such as ten words or 75 bytes.”