Typically, every part in most coherent text has some plausible reason for its presence, some function that it performs to the overall semantics of the text. Rhetorical relations, e.g. contrast, cause, explanation, describe how the parts of a text are linked to each other. Knowledge about this socalled discourse structure has been applied successfully to several natural language processing tasks. This work studies the use of rhetorical relations for Information Retrieval (IR): Is there a correlation between certain rhetorical relations and retrieval performance? Can knowledge about a document's rhetorical relations be useful to IR? We present a language model modification that considers rhetorical relations when estimating the relevance of a document to a query. Empirical evaluation of different versions of our model on TREC settings shows that certain rhetorical relations can benefit retrieval effectiveness notably (> 10 in mean average precision over a state-of-the-art baseline).
In the area of text compression, @cite_12 study the usefulness of rhetorical relations between sentences for summarisation. They find that most of the significant rhetorical relations are associated to non-discriminative sentences, i.e. sentences that are for summarisation. They report that rhetorical relations that may be intuitively perceived as highly salient do not provide strong indicators of informativeness; instead, the usefulness of rhetorical relations is in providing constraints for navigating through the text's structure. These findings are compatible with the study of Clarke & Lapata @cite_17 into constraining text compression on the basis of rhetorical relations. For a more in-depth look into the impact of individual rhetorical relations to summarisation see Teufel & Moens @cite_7 .
@cite_7 @cite_12 @cite_17
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We present a discriminative model for single-document summarization that integrally combines compression and anaphoricity constraints. Our model selects textual units to include in the summary based on a rich set of sparse features whose weights are learned on a large corpus. We allow for the deletion of content within a sentence when that deletion is licensed by compression rules; in our framework, these are implemented as dependencies between subsentential units of text. Anaphoricity constraints then improve cross-sentence coherence by guaranteeing that, for each pronoun included in the summary, the pronoun's antecedent is included as well or the pronoun is rewritten as a full mention. When trained end-to-end, our final system outperforms prior work on both ROUGE as well as on human judgments of linguistic quality. In this paper, we focus on the problem of using sentence compression techniques to improve multi-document summarization. We propose an innovative sentence compression method by considering every node in the constituent parse tree and deciding its status – remove or retain. Integer liner programming with discriminative training is used to solve the problem. Under this model, we incorporate various constraints to improve the linguistic quality of the compressed sentences. Then we utilize a pipeline summarization framework where sentences are first compressed by our proposed compression model to obtain top-n candidates and then a sentence selection module is used to generate the final summary. Compared with state-ofthe-art algorithms, our model has similar ROUGE-2 scores but better linguistic quality on TAC data. In natural language relationships between entities can asserted within a single sentence or over many sentences in a document. Many information extraction systems are constrained to extracting binary relations that are asserted within a single sentence (single-sentence relations) and this limits the proportion of relations they can extract since those expressed across multiple sentences (inter-sentential relations) are not considered. The analysis in this paper focuses on finding the distribution of inter-sentential and single-sentence relations in two corpora used for the evaluation of information extraction systems: the MUC6 corpus and the ACE corpus from 2003. In order to carry out this analysis we had to manually mark up all the management succession relations described in the MUC6 corpus. It was found that inter-sentential relations constitute 28.5 and 9.4 of the total number of relations in MUC6 and ACE03 respectively. This places upper bounds on the recall of information extraction systems that do not consider relations that are asserted across multiple sentences (71.5 and 90.6 respectively). Text summarization is one of the oldest problems in natural language processing. Popular approaches rely on extracting relevant sentences from the original documents. As a side effect, sentences that are too long but partly relevant are doomed to either not appear in the final summary, or prevent inclusion of other relevant sentences. Sentence compression is a recent framework that aims to select the shortest subsequence of words that yields an informative and grammatical sentence. This work proposes a one-step approach for document summarization that jointly performs sentence extraction and compression by solving an integer linear program. We report favorable experimental results on newswire data.