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ROUGE: A Package for Automatic Evaluation of Summaries
- Chin-Yew Lin
- Computer ScienceACL
- 25 July 2004
Four different RouGE measures are introduced: ROUGE-N, ROUge-L, R OUGE-W, and ROUAGE-S included in the Rouge summarization evaluation package and their evaluations.
Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics
The results show that automatic evaluation using unigram co-occurrences between summary pairs correlates surprising well with human evaluations, based on various statistical metrics; while direct application of the BLEU evaluation procedure does not always give good results.
Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics
Two new objective automatic evaluation methods for machine translation based on longest common subsequence between a candidate translation and a set of reference translations and relaxes strict n-gram matching to skip-bigram matching are described.
ORANGE: a Method for Evaluating Automatic Evaluation Metrics for Machine Translation
A new evaluation method, Orange, is introduced for evaluating automatic machine translation evaluation metrics automatically without extra human involvement other than using a set of reference translations.
Low-Quality Product Review Detection in Opinion Summarization
Experimental results show that the proposed approach effectively discriminates lowquality reviews from high-quality ones and enhances the task of opinion summarization by detecting and filtering low quality reviews.
Finding question-answer pairs from online forums
This paper proposes a sequential patterns based classification method to detect questions in a forum thread, and a graph based propagation method to detects answers for questions in the same thread.
Automated Text Summarization in SUMMARIST
The system’s architecture is described and details of some of its modules, many of them trained on large corpora of text, are provided.
COM: a generative model for group recommendation
A probabilistic model named COM (COnsensus Model) is proposed to model the generative process of group activities, and make group recommendations, and is able to incorporate both users' selection history and personal considerations of content factors.
The Automated Acquisition of Topic Signatures for Text Summarization
A method for automatically training topic signatures-sets of related words, with associated weights, organized around head topics, is described and illustrated with signatures the authors created with 6,194 TREC collection texts over 4 selected topics.
Joint Entity Recognition and Disambiguation
JERL, Joint Entity Recognition and Linking, is the first model to jointly optimize NER and linking tasks together completely, and in experiments on the CoNLL’03/AIDA data set, JERL outperforms state-of-art N ER and linking systems.