Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks209 from public data
- ROUGE: A Package for Automatic Evaluation of Summaries22,250
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 statistics1,928
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 statistics946
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.
- The automated acquisition of topic signatures for text summarization553
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.
- Automated Text Summarization in SUMMARIST544
The system’s architecture is described and details of some of its modules, many of them trained on large corpora of text, are provided.
- ORANGE429
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 Summarization424
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.
- Question Answering in Webclopedia.322
The QA Typology contains 94 nodes, of which 47 are leaf nodes; each Typology node has been annotated with examples and typical patterns of expression of both Question and Answer, as indicated in Figure 3.
- Identifying topics by position318
The automated training and evaluation of an Optimal Position Policy is described, a method of locating the likely positions of topic-bearing sentences based on genre-specific regularities of discourse structure that can be used in applications such as information retrieval, routing, and text summarization.
- Manual and automatic evaluation of summaries309
This paper shows the instability of the manual evaluation of summaries, and investigates the feasibility of automated summary evaluation based on the recent BLEU method from machine translation using accumulative n-gram overlap scores between system and human summaries.
- Toward semantics-based answer pinpointing303
The treatment of questions (Question-Answer Typology, question parsing, and results) in the Weblcopedia question answering system are described.
- Automated text summarization and the SUMMARIST system280
A preliminary typology of summaries in general is presented; a description of the current and planned modules and performance of the SUMMARIST automated multilingual text summarization system is described; and three methods to evaluate summaries are discussed.
- Finding question-answer pairs from online forums278
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.
- From single to multi-document summarization272
NeATS is a multi-document summarization system that attempts to extract relevant or interesting portions from a set of documents about some topic and present them in coherent order.
- What's in a name?264
The analysis shows that the alias-disambiguation solution can address a major part of the user linking problem in terms of the coverage of true pairwise decisions and that the rareness and commonness of usernames can help user linking.
- Joint Entity Recognition and Disambiguation248
JERL 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 NER and linking systems.
- Joint Named Entity Recognition and Disambiguation243
JERL 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 NER and linking systems.
- Automated Summarization Evaluation with Basic Elements.216
This paper describes a framework in which summary evaluation measures can be instantiated and compared, and implements a specific evaluation method using very small units of content, called Basic Elements that address some of the shortcomings of ngrams.
- COM215
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.
- Automatically Solving Number Word Problems by Semantic Parsing and Reasoning200
A new meaning representation language is designed to bridge natural language text and math expressions and a CFG parser is implemented based on 9,600 semi-automatically created grammar rules.
- Looking for a Few Good Metrics: ROUGE and its Evaluation191
The validity of the evaluation method used in the Document Understanding Conference (DUC) is discussed and five different ROUGE metrics are evaluated included in the RouGE summarization evaluation package using data provided by DUC.
- Training a selection function for extraction183
Comparisons of performance of several heuristics in generating informative generic/query-oriented extracts for newspaper articles are compared in order to learn how topic prominence affects the performance of each heuristic.
- Automated Text Summarization176
This tutorial will review the state of the art in automatic summarization, and will discuss and critically evaluate current approaches to the problem, including manual evaluation procedures used in DUC, the factoid and pyramid method reference summary creation procedures and fully automatic evaluation method such as ROUGE.
- Searching Questions by Identifying Question Topic and Question Focus170
Experimental results indicate that the approach of identifying question topic and question focus for search significantly outperforms the baseline methods such as Vector Space Model (VSM) and Language Model for Information Retrieval (LMIR).
- How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation158
A large-scale dataset is built which is more than 9 times the size of previous ones, and contains many more problem types, and semi-automatically obtained from community question-answering web pages.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.