Extracting Keywords with TextRank and Weighted Word Positions
Liu Zhuchen1, Chen Hao2, Yu Yanhua1(), Li Jie1
1School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China 2China Shipbuilding Industry System Engineering Research Institute, Beijing 100094, China
[Objective] This study integrates the position and distance attributes of words into the TextRank model, aiming to extract keywords from single document more effectively. [Methods] First, we constructed the word graph for candidates based on the TextRank method. Then, we merged the position information of the words, and calculated their probability transfer matrix. Finally, we obtained the points of candidate words by iterative calculation, and retrieved the top K of keywords with the highest scores. [Results] We found that the weighted TextRank method yielded better results than the traditional algorithms. When the K values were 3, 5, 7 and 10, the increment of F value were 1.29%, 3.14%, 5.43% and 5.88% respectively. [Limitations] This study did not include knowledge base and did not fully utilize the external lexical relationship information. [Conclusions] The position and distribution of words can help us extract keywords more effectively.
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Liu Zhuchen,Chen Hao,Yu Yanhua,Li Jie. Extracting Keywords with TextRank and Weighted Word Positions. Data Analysis and Knowledge Discovery, 2018, 2(9): 74-79.
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doi: 10.3969/j.issn.1001-3695.2016.03.035
