A Topic-driven Summarization using K-mean
... query-focused multi-document summarization that uses k-mean clustering, term-frequency and
inversesentence- frequency method for sentence weighting to rank the sentences of the
documents with respect to a given query. The proposed method finds the proximity of
documents and query, and later uses thi ...
Extracting Attractive Summaries for News Propagation on Microblogs
... concerned by a large number of people. The common way
of releasing news on microblogs is to post human-edited
short summaries on microblog sites, and the corresponding
full news articles can be found via a URL link to external
news portals. Usually, an attractive news summary will
often bring more p ...
... Machine Reading (MR) is very different from current
semantic NLP research areas such as Information
Extraction (IE) or Question Answering (QA). Many NLP
tasks utilize supervised learning techniques, which rely on
hand-tagged training examples. For example, IE systems
often utilize extraction rules l ...
... thus leading to better results in shorter time.
Keyphrases are representative of the complete
document. Irrelevant results can be reduced if search
is based on keyphrases.
Descriptive Data Summarization
... Trimmed mean
– A major problem with the mean is its sensitivity to extreme
(e.g., outlier) values.
– Even a small number of extreme values can corrupt the
– the trimmed mean is the mean obtained after cutting off
values at the high and low extremes.
– For example, we can sort the values and re ...
Multi-Sentence Compression: Finding Shortest Paths
... of news classification and clustering with a production quality. Apart from that, it is a rich source
of multilingual data.
We collected news clusters in English and
Spanish, 10-30 articles each, 24 articles on average. To get sets of similar sentences we aggregated first sentences from every articl ...
Can Word Probabilities from LDA be Simply Added up to Represent
... This research was supported by the National Science Foundation
(DRK-12-0918409, 1108845), the Institute of Education Sciences
(R305H050169, R305B070349, R305A080589, R305A080594,
R305G020018, R305C120001), Army Research Lab (W911INF12-2-0030), and the Office of Naval Research (N00014-00-1-0600,
A New Entity Salience Task with Millions of Training Examples
... tagger and dependency parser, comparable in accuracy to the current Stanford dependency parser
(Klein and Manning, 2003); an NP extractor that
uses POS tags and dependency edges to identify
a set of entity mentions; a coreference resolver,
comparable to that of Haghighi and Klein, (2009)
for cluster ...
in the document - XP
... organization. Both incoming and internally generated documents are
automatically abstracted, characterized by a word pattern, and sent
automatically to appropriate action points.”
Automatic summarization is the process of reducing a text document with a computer program in order to create a summary that retains the most important points of the original document. As the problem of information overload has grown, and as the quantity of data has increased, so has interest in automatic summarization. Technologies that can make a coherent summary take into account variables such as length, writing style and syntax. Automatic data summarization is a very important area within machine learning and data mining. Summarization technologies are used today, in a large number of sectors in industry today. An example of the use of summarization technology is search engines such as Google. Other examples include document summarization, image collection summarization and video summarization. The main idea of summarization is to find a representative subset of the data, which contains the information of the entire set. Document summarization, tries to automatically create a representative summary or abstract of the entire document, by finding the most informative sentences. Similarly, in image summarization the system finds the most representative and important (or salient) images. Similarly, in consumer videos one would want to remove the boring or repetitive scenes, and extract out a much shorter and concise version of the video. This is also important, say for surveillance videos, where one might want to extract out only important events in the recorded video, since most of the events are uninteresting with nothing going on.Generally, there are two approaches to automatic summarization: extraction and abstraction. Extractive methods work by selecting a subset of existing words, phrases, or sentences in the original text to form the summary. In contrast, abstractive methods build an internal semantic representation and then use natural language generation techniques to create a summary that is closer to what a human might generate. Such a summary might contain words not explicitly present in the original. Research into abstractive methods is an increasingly important and active research area, however due to complexity constraints, research to date has focused primarily on extractive methods. In some application domains, extractive summarization makes more sense. Examples of these include image collection summarization and video summarization.