| tech,5-1-H05-1115,ak | literature . We consider the problem of <term> | question-focused sentence retrieval | </term> from complex news articles describing | #5754 We consider the problem ofquestion-focused sentence retrieval from complex news articles describing multi-event stories published over time. | |
| other,0-2-H05-1115,ak | multi-event stories published over time . <term> | Annotators | </term> generated a list of <term> questions | #5768 We consider the problem of question-focused sentence retrieval from complex news articles describing multi-event stories published over time.Annotators generated a list of questions central to understanding each story in our corpus. | |
| other,5-2-H05-1115,ak | Annotators </term> generated a list of <term> | questions | </term> central to understanding each story | #5773 Annotators generated a list ofquestions central to understanding each story in our corpus. | |
| lr,13-2-H05-1115,ak | to understanding each story in our <term> | corpus | </term> . Because of the dynamic nature of | #5781 Annotators generated a list of questions central to understanding each story in ourcorpus. | |
| other,10-3-H05-1115,ak | dynamic nature of the stories , many <term> | questions | </term> are time-sensitive ( e.g . How many | #5793 Because of the dynamic nature of the stories, manyquestions are time-sensitive (e.g. How many victims have been found?) | |
| other,2-4-H05-1115,ak | victims have been found ? ) Judges found <term> | sentences | </term> providing an <term> answer </term> to | #5809 Judges foundsentences providing an answer to each question. | |
| other,5-4-H05-1115,ak | <term> sentences </term> providing an <term> | answer | </term> to each <term> question </term> . To | #5812 Judges found sentences providing ananswer to each question. | |
| other,8-4-H05-1115,ak | providing an <term> answer </term> to each <term> | question | </term> . To address the <term> sentence retrieval | #5815 Judges found sentences providing an answer to eachquestion. | |
| other,3-5-H05-1115,ak | <term> question </term> . To address the <term> | sentence retrieval problem | </term> , we apply a <term> stochastic , graph-based | #5820 To address thesentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of the textual units, which was previously used successfully for generic summarization. | |
| tech,10-5-H05-1115,ak | retrieval problem </term> , we apply a <term> | stochastic , graph-based method | </term> for comparing the <term> relative importance | #5827 To address the sentence retrieval problem, we apply astochastic , graph-based method for comparing the relative importance of the textual units, which was previously used successfully for generic summarization. | |
| measure(ment),17-5-H05-1115,ak | graph-based method </term> for comparing the <term> | relative importance | </term> of the <term> textual units </term> , | #5834 To address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing therelative importance of the textual units, which was previously used successfully for generic summarization. | |
| other,21-5-H05-1115,ak | <term> relative importance </term> of the <term> | textual units | </term> , which was previously used successfully | #5838 To address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of thetextual units, which was previously used successfully for generic summarization. | |
| tech,30-5-H05-1115,ak | was previously used successfully for <term> | generic summarization | </term> . Currently , we present a topic-sensitive | #5847 To address the sentence retrieval problem, we apply a stochastic, graph-based method for comparing the relative importance of the textual units, which was previously used successfully forgeneric summarization. | |
| measure(ment),18-6-H05-1115,ak | that it can outperform a competitive <term> | baseline | </term> , which compares the <term> similarity | #5868 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitivebaseline, which compares the similarity of each sentence to the input question via IDF-weighted word overlap. | |
| measure(ment),23-6-H05-1115,ak | baseline </term> , which compares the <term> | similarity | </term> of each <term> sentence </term> to the | #5873 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares thesimilarity of each sentence to the input question via IDF-weighted word overlap. | |
| other,26-6-H05-1115,ak | the <term> similarity </term> of each <term> | sentence | </term> to the <term> input question </term> | #5876 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of eachsentence to the input question via IDF-weighted word overlap. | |
| other,29-6-H05-1115,ak | of each <term> sentence </term> to the <term> | input question | </term> via <term> IDF-weighted word overlap | #5879 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of each sentence to theinput question via IDF-weighted word overlap. | |
| measure(ment),32-6-H05-1115,ak | the <term> input question </term> via <term> | IDF-weighted word overlap | </term> . In our experiments , the method | #5882 Currently, we present a topic-sensitive version of our method and hypothesize that it can outperform a competitive baseline, which compares the similarity of each sentence to the input question viaIDF-weighted word overlap. | |
| measure(ment),8-7-H05-1115,ak | experiments , the method achieves a <term> | TRDR score | </term> that is significantly higher than | #5894 In our experiments, the method achieves aTRDR score that is significantly higher than that of the baseline. | |
| measure(ment),18-7-H05-1115,ak | significantly higher than that of the <term> | baseline | </term> . Following recent developments in | #5904 In our experiments, the method achieves a TRDR score that is significantly higher than that of thebaseline. |
