Cohesion
Cohesion is a device for ``sticking together'' different parts of the text. Cohesion is achieved through the use of semantically related terms, co-reference, elipsis and conjunctions. (Barzilay and Elhadad, 1997)
Gold standard
We will call the criteria of what constitutes success the gold standard, and the set of sentences that fulfill these criteria the gold standard sentences. Apart from evaluation, a gold standard is also needed for supervised learning. (cited in Teufel and Moens, 1997)
In Kupiec et al. (1995), a gold standard sentence is a sentence in the source text that is matched with a summary sentence on the basis of semantic and syntactic similarity. (cited in Teufel and Moens, 1997)
Intrinsic/Extrinsic Evaluations
Evaluation of summarization systems can be intrinsic or extrinsic (Jones & Galliers 1996). Intrinsic methods measure a system's quality; extrinsic methods measure a system's performance in a particular task.(cited in Jing and McKeown, 1998)
Compression Ratio
"how much shorter is the summary than the original" (Chapter 3: Cross-lingual Information Extraction and Automated Text Summarization. Ed. E. Hovy)
Retention Ratio
"how much information have you retained" (Chapter 3: Cross-lingual Information Extraction and Automated Text Summarization. Ed. E. Hovy)
Extract vs Abstract
"an Extract is a selection of some of the material of the original, while an Abstract is a condensation and reformulation of the original" (Chapter 3: Cross-lingual Information Extraction and Automated Text Summarization. Ed. E. Hovy)
Generic vs User-Specific (Query-based)
"a Generic summary provides the author’s point of view, while a Query-based summary focuses on material of interest to the user" (Chapter 3: Cross-lingual Information Extraction and Automated Text Summarization. Ed. E. Hovy)
Informative vs Indicative
"an Informative summary reflects the content of the original text, possibly spelling out the arguments, while an Indicative summary merely provides an indication of what the original was about" (Chapter 3: Cross-lingual Information Extraction and Automated Text Summarization. Ed. E. Hovy)
"Just-the-News" vs. Background
"a Just-the-News summary provides just the newest facts, assuming the reader is familiar with the topic, while a Background summary teaches about the topic" (Chapter 3: Cross-lingual Information Extraction and Automated Text Summarization. Ed. E. Hovy)
Neutral vs. Biased
"a Neutral summary tries to be objective, while a Biased summary extracts and formulates the content from some point of view. " (Chapter 3: Cross-lingual Information Extraction and Automated Text Summarization. Ed. E. Hovy)
DARPA:
Defense Advances Research Projects Agency
IE :
Information Extraction
IR:
Information Retrieval
MT:
Machine Translation
MUC:
Message Understanding Conference (A TIPSTER initiative)
NIST:
National Institute of Standards and Technology
SUMMAC:
SUMMArization Conference (A TIPSTER initiative)
a research effort examining Document Detection (TREC), Information Extraction (MUC) and Summarization (SUMMAC)
TREC:
Text REtrieval Conference (A TIPSTER initiative)