Abstract
With the advent of the Internet/WWW and the proliferation of textual news, richmedia, social networking, et cetera. there has been an unprecedented surge in the content on the web. With huge amount of information available on the world wide web, there is a pressing need to have ‘Information Access’ systems that would help
users with an information need in providing the relevant information in a concise, pertinent format. There are various modes of Information Access including Information Retrieval, Text Mining, Machine Translation, Text Categorization, Text Summarization, et cetera. In this thesis, we study certain aspects of “Text Summarization” as a technology for Information Access’.
Majority of the recent work in the area of Text Summarization addresses the subproblems like Ranked Sentence Ordering, Generic Single Document Summarization, Generic Multi-Document Summarization, Query-Focused Multi-Document
Summarization, Query-Focused Update Summarization. In this thesis we focus on
two tasks, specifically “Query-Focused Multi-Document Summarization (QFMDS)” and “Query-Focused Update Summarization (QFUS)”. The QFMDS task has generated a lot of community interest for obvious reasons of being closer to a real world application of open domain question answering. Given a set of N relevant documents on a general topic and a specific query (or information need) within the
topic, the task is to generate a relevant and fluent 250 word summary. The focus of this thesis lies around four issues dealing with query-focused multi-document summarization. They are:
1. Impact of query-bias on summarization
2. Simple and strong baselines for update summarization
3. Language modeling extension to update summarization
4. An automated intrinsic content evaluation measure
In this thesis we identify two dependent yet different terms ‘query-bias’ and ‘query-focus’ with respect to the QFMDS task and show that most of the automated summarization systems are trying to be query-biased rather than being queryfocused.
In the context of this problem, we show evidence from multiple sources to display the inherent bias introduced by the automated systems. First, we theoretically explain how a na¨ıve classifier based summarizer can be enhanced greatly by biasing the algorithm to use just the query-biased sentences. Second, on an ‘information nugget’ based evaluation data, we show that most of the participating
systems were query-biased. Third, we further build formal generative models, namely binomial and multinomial models, to model the likelihood of a system being query-biased. Such a modeling revealed a high positive correlation between ‘a system being query-biased’ and ‘automated evaluation score’. Our results also underscore the difference between human and automated summaries. We show that
when asked to produce query-focused summaries humans do not rely to the same extent on the repetition of query-terms.
The QFUS task is a natural extension to the QFMDS task. In update summarization task a series of news stories (news stream) on a particular topic are tracked over a period of time and are available for summarization. Two or more sets of documents are available: an initial set, followed by multiple updated sets of news stories. The problem is to generate query-focused multi-document summaries for the initial set and then produce update summaries for the updated sets assuming the user has already read the preceding sets of documents. This is a relatively new task and our work in this context is two fold. First, we defined a sentence position based baseline summarizer which is genre dependent. In the context of the new task, we argue that current baseline in the update summarization task is a poor performer
in content evaluations and hence cannot help in tracking the progress of the field. A stronger baseline such as the one based on “sentence position” would be robust and be able to help track the progress. Second, we describe a generic extension to language modeling based approaches to tailor them towards the update summarization task. We showed that a simple Context Adjustment (CA) based on ‘stream
dynamics’ could help in generation of better summaries when compared to the base language modeling approach.
In Text Summarization, like in any other Information Access methodologies, evaluation is a crucial component of the system development process. In language technologies research, Automated Evaluation is often viewed as supplementary to
the task itself since knowing how to evaluate would lead to knowing how to perform the task. In the case of summarization, knowing how best to evaluate summaries would help in knowing how best to summarize. Usually, manual evaluations are used to evaluate summaries and compare performance of different systems against each other. However, these manual evaluations are time consuming and difficult to repeat, hence infeasible. Keep