  Automatic summarization is the process of reducing a text document in order
to generate a summary that retains the most important points of the original
document. In this work, we study two problems - i) summarizing a text document
as set of keywords/caption, for image recommedation, ii) generating opinion
summary which good mix of relevancy and sentiment with the text document.
Intially, we present our work on an recommending images for enhancing a
substantial amount of existing plain text news articles. We use probabilistic
models and word similarity heuristics to generate captions and extract
Key-phrases which are re-ranked using a rank aggregation framework with
relevance feedback mechanism. We show that such rank aggregation and relevant
feedback which are typically used in Tagging Documents, Text Information
Retrieval also helps in improving image retrieval. These queries are fed to the
Yahoo Search Engine to obtain relevant images 1. Our proposed method is
observed to perform better than all existing baselines. Additonally, We propose
a set of submodular functions for opinion summarization. Opinion summarization
has built in it the tasks of summarization and sentiment detection. However, it
is not easy to detect sentiment and simultaneously extract summary. The two
tasks conflict in the sense that the demand of compression may drop sentiment
bearing sentences, and the demand of sentiment detection may bring in redundant
sentences. However, using submodularity we show how to strike a balance between
the two requirements. Our functions generate summaries such that there is good
correlation between document sentiment and summary sentiment along with good
ROUGE score. We also compare the performances of the proposed submodular
functions.
