diff --git a/mypaper-final.tex b/mypaper-final.tex
index 2191e70e084bc7009c68ecb296dd2c4d76505fa5..2908c42ec266f745c41cc80928542dfc8da4fed0 100644
--- a/mypaper-final.tex
+++ b/mypaper-final.tex
@@ -106,9 +106,30 @@
 \maketitle
 \begin{abstract}
 
-
-Entity-centric information processing requires complex pipelines involving both natural language processing and information retrieval components. In entity-centric stream filtering and ranking, the pipeline involves four  important stages: filtering, classification, ranking(scoring)  and evaluation. Filtering is an important step  that creates a manageable working set of documents  from a  web-scale corpus for the next stages.  It thus  determines the performance of the overall system.  Keeping the subsequent steps constant, we  zoom in on the filtering stage and conduct an in-depth analysis of the  main components of cleansing, entity profiles, relevance levels, category of documents and entity types with a view to understanding  the factors and choices that affect filtering performance. The study demonstrates the most  effective entity profiling,  identifies those relevant documents that defy filtering and conducts manual examination into their contents. The paper classifies the ways unfilterable documents 
-are mentioned in text and estimates the practical upper-bound of recall in  entity-based filtering.  
+Cumulative citation recommendation refers to the problem faced by
+knowledge base curators, who need to continuously screen the media for
+updates regarding the knowledge base entries they manage. Automatic
+system support for this entity-centric information processing problem
+requires complex pipe\-lines involving both natural language
+processing and information retrieval components. The default pipeline
+involves four stages: filtering, classification, ranking (or scoring),
+and evaluation. Filtering is an initial step, that reduces the
+web-scale corpus of news and other relevant information sources that
+may contain entity mentions into a working set of documents that should
+be more manageable for the subsequent stages.
+This step has a large impact on the recall that can be achieved.
+Keeping the subsequent steps constant, we therefore zoom in into the
+filtering stage, and conduct an in-depth analysis of the main design
+decisions here:
+cleansing noisy web data, the methods to create entity profiles, the
+types of entities of interest, document type, and the grade of
+relevance of the document-entity pair under consideration.
+We analyze how these factors (and the design choices made in their
+corresponding system components) affect filtering performance.
+We identify and characterize the relevant documents that do not pass
+the filtering stage by examing their contents. This way, we give
+estimate a practical upper-bound of recall for entity-centric stream
+filtering.  
 
 \end{abstract}
 % A category with the (minimum) three required fields
@@ -692,8 +713,12 @@ Wikipedia's canonical partial is the best entity profile for Wikipedia entities.
 
 
 
+<<<<<<< HEAD
 The deltas between entity profiles, relevance ratings, and document categories reveal four differences between Wikipedia and Twitter entities. 1) For Wikipedia entities, the difference between canonical partial and canonical is higher(16.1\%) than between name-variant partial and  name-variant(18.3\%).  This can be explained by saturation. This is to mean that documents have already been extracted by  name-variants and thus using their partials does not bring in many new relevant documents.  2) Twitter entities are mentioned by name-variant or name-variant partial and that is seen in the high recall achieved  compared to the low recall achieved by canonical(or their partial). This indicates that documents (specially news and others) almost never use user names to refer to Twitter entities. Name-variant partials are the best entity profiles for Twitter entities. 3) However, comparatively speaking, social documents refer to Twitter entities by their user names than news and others suggesting a difference in 
 adherence to standard in names and naming. 4) Wikipedia entities achieve higher recall and higher overall performance. 
+=======
+The deltas between entity profiles, relevance ratings, and document categories reveal four differences between Wikipedia and Twitter entities. 1) For Wikipedia entities, the difference between canonical partial and canonical is higher(16.1\%) than between name-variant partial and  name-variant(8.3\%).  This can be explained by saturation. This is to mean that documents have already been extracted by  name-variants and thus using their partials does not bring in many new relevant documents.  2) Twitter entities are mentioned by name-variant or name-variant partial and that is seen in the high recall achieved  compared to the low recall achieved by canonical(or their partial). This indicates that documents (specially news and others) almost never use user names to refer to Twitter entities. Name-variant partials are the best entity profiles for Twitter entities. 3) However, comparatively speaking, social documents refer to Twitter entities by their user names than news and others suggesting a difference in adherence to standard in names and naming. 4) Wikipedia entities achieve higher recall and higher overall performance. 
+>>>>>>> 60fbfbab0287ab72519987bdcba3adb5a0aa93c8
 
 The high recall and subsequent higher overall performance of Wikipedia entities can  be due to two reasons. 1) Wikipedia entities are relatively well described than Twitter entities. The fact that we can retrieve different name variants from DBpedia is a measure of relatively rich description. Rich description plays a role in both filtering and computation of features such as similarity measures in later stages of the pipeline.   By contrast, we have only two names for Twitter entities: their user names and their display names which we collect from their Twitter pages. 2) There is not DBpedia-like resource for Twitter entities from which alternative names cane be collected.   
 
