1,859 research outputs found
An Effective Sentence Ordering Approach For Multi-Document Summarization Using Text Entailment
With the rapid development of modern technology electronically available textual information has increased to a considerable amount. Summarization of textual inform ation manually from unstructured text sources creates overhead to the user, therefore a systematic approach is required. Summarization is an approach that focuses on providing the user with a condensed version of the origina l text but in real time applicat ions extended document summarization is required for summarizing the text from multiple documents. The main focus of multi - document summarization is sentence ordering and ranking that arranges the collected sentences from multiple document in order to gene rate a well - organized summary. The improper order of extracted sentences significantly degrades readability and understandability of the summary. The existing system does multi document summarization by combining several preference measures such as chronology, probabilistic, precedence, succession, topical closeness experts to calculate the preference value between sentences. These approach to sent ence ordering and ranking does not address context based similarity measure between sentences which is very ess ential for effective summarization. The proposed system addresses this issues through textual entailment expert system. This approach builds an entailment model which incorpo rates the cause and effect between sentences in the documents using the symmetric measure such as cosine similarity and non - symmetric measures such as unigram match, bigram match, longest common sub - sequence, skip gram match, stemming. The proposed system is efficient in providing user with a contextual summary which significantly impro ves the readability and understandability of the final coherent summa
Recommended from our members
Advances in statistical script learning
When humans encode information into natural language, they do so with the
clear assumption that the reader will be able to seamlessly make inferences
based on world knowledge. For example, given the sentence ``Mrs. Dalloway said
she would buy the flowers herself,'' one can make a number of probable
inferences based on event co-occurrences: she bought flowers, she went to a
store, she took the flowers home, and so on.
Observing this, it is clear that many different useful natural language
end-tasks could benefit from models of events as they typically co-occur
(so-called script models).
Robust question-answering systems must be able to infer highly-probable implicit
events from what is explicitly stated in a text, as must robust
information-extraction systems that map from unstructured text to formal
assertions about relations expressed in the text. Coreference resolution
systems, semantic role labeling, and even syntactic parsing systems could, in
principle, benefit from event co-occurrence models.
To this end, we present a number of contributions related to statistical
event co-occurrence models. First, we investigate a method of incorporating
multiple entities into events in a count-based co-occurrence model. We find that
modeling multiple entities interacting across events allows for improved
empirical performance on the task of modeling sequences of events in documents.
Second, we give a method of applying Recurrent Neural Network sequence models
to the task of predicting held-out predicate-argument structures from documents.
This model allows us to easily incorporate entity noun information, and can
allow for more complex, higher-arity events than a count-based co-occurrence
model. We find the neural model improves performance considerably over the
count-based co-occurrence model.
Third, we investigate the performance of a sequence-to-sequence encoder-decoder
neural model on the task of predicting held-out predicate-argument events from
text. This model does not explicitly model any external syntactic information,
and does not require a parser. We find the text-level model to be competitive in
predictive performance with an event level model directly mediated by an
external syntactic analysis.
Finally, motivated by this result, we investigate incorporating features derived
from these models into a baseline noun coreference resolution system. We find
that, while our additional features do not appreciably improve top-level
performance, we can nonetheless provide empirical improvement on a number of
restricted classes of difficult coreference decisions.Computer Science
- …