Literature Survey
5.3 Future Work
There is still much work that should be done in the future. This section outlines future work that could extend the applicability and performance of the proposed summarization framework, including:
(1) The employment of natural language processing (NLP) techniques:
This thesis does not have much NLP techniques to help understand and analyze the input texts. We suppose that the use of NLP techniques, for example, information extraction, sentence parsing, lexical chains, co-reference chains, etc., can directly benefit the identification of text entities and their relationships, and hence leads to better understanding of texts and content selection.
(2) The utilization of domain knowledge and external resources:
This thesis uses no domain knowledge in the summarization process since it targets at general-purpose summarization of documents in public domains. Such a summarization framework would apparently not work well in all domains. It is expected that domain knowledge and external resources, such as patient record and disease information in medical domains, and terminologies in financial and sports areas, should improve the analysis of texts in particular domains.
(3) The consideration of language properties:
This thesis performs no deep linguistic analysis particular to languages and thus the proposed framework can be practically applied to documents in any languages, for which the decomposition of texts into word units in preprocessing can be done. It is believed that the framework is applicable to
multilingual/cross-lingual multidocument summarization as well, provided that machine translation modules are available, or the relevance of text portions in different languages can be determined.
(4) The investigation of new similarity measures:
This thesis exploits the cosine similarity metric (in vector space model and in latent semantic space) to measure the relations between each pair of sentences, as well as the relevance between a sentence and the query. We expect that more advanced techniques of assessing similarity, which incorporate word semantics and relations, will be easily integrated in the summarization model. In addition, using words or phrases with similar meanings to expand the user query will obviously profit the identification of query-biased sentences.
(5) The exploration of new surface-level features:
This thesis examines a subset of surface-level features and various combinations of them to determine the informativeness of sentences (or the likelihood of sentences of being part of the summary) in summarization.
Nevertheless, it is worth studying to discover other effective features, to identify the effect of a feature to summarization, as well as to investigate the relations between different features for feature selection.
(6) The application of machine learning techniques:
This thesis combines different features in an unsupervised manner to yield a sentence scoring function, for which parameters are tuned empirically. As more and more standard collections for training and test on evaluation of
summarization methods have been established recently, we intend to apply machine learning techniques to automatically learn an effective sentence scoring model from training data.
(7) The improvement of the summary quality:
This thesis adopts the most common technique in summarization, namely sentence extraction, to create extractive summaries. However, this strategy does not guarantee good summary quality in terms of coherence, cohesion, and overall organization, even though it may include good content in the summaries.
Fortunately, techniques to improve the quality of summaries, such as, information fusion and reformulation by natural language generation to produce abstractive summaries, passage simplification/compression to remove parts of, for example, a sentence without disturbing its understandability or underlying meaning, information ordering to yield coherent summaries, and anaphora resolution and time annotation to produce summaries with good readability, have proven successful in some degree.
(8) The use of different strategies for different types of the input document clusters:
This thesis uses the same strategy to deal with different types of the input document clusters. Such a single strategy has shown promising in evaluation.
However, we believe that a first step to examine the types of the document clusters, and then to process the documents using different strategies will probably generate better summaries. For instance, news articles can be classified into on the same event, on topically related but different events, natural disaster, biography, etc., for which different summarization strategies should be decided.
(9) The enhancement of visualization:
This thesis does not provide visualization of summaries. Obviously, it could be beneficial to the user by presenting visual information related to the content in summaries. The following gives some visualization examples in news summarization: the visualization of the spatial information, indicated in the summary, on a geographical map; a visual summary with the x-axis representing the timeline, the y-axis representing the location, and the (x, y) point labeled with keywords of news events, linking to the corresponding text summary.
(10) The addition of user interaction mechanisms:
This thesis only provides the user with simple controls, such as the length of the summary, and the summary type in generic and query-focused, over the summarization process. One shortage of such a system is the lack of dynamic response to the user’s need. Therefore, future work will add user interaction mechanisms into the proposed summarization framework. For instance, the linking of a summary sentence to the original document or to the most relevant sentences in the documents; the zoom-in and zoom-out of topics of a summary in the hierarchical structure; the control to obtain preferred summaries by relevance feedback of user interests.
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