This paper proposes a text summarization approach for factual reports using a deep learning model. This approach consists of three phases: feature extraction, feature enhancement, and summary generation, which work together to assimilate core information and generate a coherent, understandable summary. We are exploring various features to improve the set of sentences selected for the summary, a...

In this thesis, I design a Maximum Coverage problem with KnaPsack constraint (MCKP) based model for extractive multi-document summarization. The model integrates three measures to detect important sentences including Coverage, rewards sentences in regards to their representative level of the whole document, Relevance, focuses to select sentences that related to the given query, and Compression,...

The problem of text summarization for a collection of documents is defined as the problem of selecting a small subset of sentences so that the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a linear model for the problem of text summarization, where we strive to obtain a summary that preserves the information coverage as much a...

This paper is about a system that extracts principal content words from speech-recognized transcripts of voicemail messages and classifies them into proper names, telephone numbers, dates/times and ‘other’. The short text summaries generated are suitable for mobile messaging applications. The system uses a set of classifiers to identify the summary words, with each word being identified by a ve...

PURPOSE Ambrisentan (ABS) is an antihypertensive drug used in the treatment of pulmonary atrial hypertension. The survey of literature for ABS revealed only two spectrophotometric methods for its quantification. The reported methods lack the sensitivity. This study is aimed at developing two sensitive extractive spectrophotometric methods for the determination of ABS in bulk and in tablets. M...

Sophisticated documents like legal cases and biomedical articles can contain unusually long sentences. Extractive summarizers can select such sentences— potentially adding hundreds of unnecessary words to the summary—or exclude them and lose important content. Sentence simplification or compression seems on the surface to be a promising solution. However, compression removes words before the se...